Alerting predicted accidents between driverless cars

ABSTRACT

This patent application discloses methods and systems for alerting computerized motor-vehicles about predicted accidents. In an example method, a motor vehicle alerts another motor vehicle about a predicted accident, even though that accident is between the alerting car and a third motor vehicle—for example, the alert is transmitted by non-visual electromagnetic (EM) radiation. When an adjacent motor vehicle receives such accident alert and determines it might itself be hit, it will react so as to minimize its chances of being hit or at least to minimize the damage if it is being hit. Optionally, one or more of the motor vehicles has an onboard device for measuring a blood-alcohol level of a human driver thereof. The measured blood-alcohol level may be used to compute a probability of an occurrence of an accident and/or may be included in one or more of the transmitted accident alerts.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation-in-part of both (i)PCT/IB2016/053102 filed on May 26, 2016 and (ii) U.S. patent applicationSer. No. 15/165,668 filed on May 26, 2016, each of which areincorporated herein by reference in their entireties. This patentapplication also claims the benefit of U.S. Provisional PatentApplication No. 62/166,795 filed on May 27, 2015, which is incorporatedherein by reference in its entirety.

BACKGROUND

A driverless vehicle (also known as “autonomous vehicle”, “self-drivingvehicle” and “robotic vehicle”) is a vehicle that is capable of sensingits environment and navigating without human input.

The idea of driverless cars was predicted by many science fiction andnon-science fiction writers a long time ago. In recent years, this ideahas been actualized, with many auto makers and research groups buildingsuch cars and conducting experiments with them. Publications aboutself-driving cars include (i) “INSIDE GOOGLE'S QUEST TO POPULARIZESELF-DRIVING CARS,” By Adam Fisher, Popular Science (Posted Sep. 18,2013) about Google's driverless car, and (ii) “NASA AND NISSAN JOINFORCES TO BUILD SELF-DRIVING VEHICLES FOR EARTH AND SPACE” by AlexDavies, Wired.com (Posted Jan. 8, 2015) about Nissan's and NASA'sdriverless car.

The term driverless or autonomous motor-vehicles also includessemi-autonomous vehicles where a human driver may be available insidethe motor-vehicle and may intervene with or take control of the drivingif he so decides, as long as the motor-vehicle has a fully autonomousmode in which it navigates and drives without human input.

Obviously, a major concern with driverless cars is the avoidance ofaccidents where two or more cars are involved in a collision. Thechallenge is especially difficult under heavy traffic conditions, whenmany cars are driving in a convoy, continuously braking, deceleratingand accelerating according to traffic events and road conditions.

First attempts for developing a driverless car relied on equipping thecar with sensors capable of detecting events in all directions. Forexample, front-looking cameras and proximity sensors residing within agiven car may monitor the car in front of it, looking for an indicationit is braking or otherwise slowing down. If such an event is detected.and there is a danger of the given car hitting the car in front of it,the driving logic automatically slows down the given car or even bringsit to a complete stop. Independently of that, rearward-looking camerasand proximity sensors are watching the car behind the given car, lookingfor an indication it is accelerating. If such an event is detected, andthere is a danger of the given car being hit by the car behind it, thedriving logic automatically accelerates the given car or otherwisemaneuvers it to avoid being hit. Additional cameras and sensors may bedirected sideways so as to watch for potential dangers from otherdirections.

It was soon found out that relying only on a car's own sensors is notgood enough. If the car behind us is accelerating, it takes some timebefore the driving logic in our car knows about the event because of thetime required for the sensors and the signal processing circuitry toidentify it. As avoiding an accident depends on a quick response by ourcar, the time lost while identifying a traffic event might be crucial.

The solution adopted for solving the above problem is to have each carwirelessly transmit information about itself so that adjacent cars canreceive that information and use it for taking their decisions. Forexample, when a driver starts to press down on his braking pedal the carimmediately reports this event by transmitting a braking alert, withoutwaiting for the braking process to actually slow down the car. The carimmediately behind the braking car does not have to wait until itssensors detect an actual slowdown of the car in front of it or alighting of its braking lights but can immediately reduce its speedbased on the received alert. This way the response time available for acar to avoid an accident is longer and the risk of unavoidable accidentsis reduced. In addition to reporting braking status a car may alsoreport its location, its speed, its acceleration, failures it may haveand other information that may be useful for other cars for assessingtheir risks and taking their decisions.

Typically driverless cars only listen to information from the car infront of them, considering it to be the main source of danger. In suchcase the communication is established only between adjacent cars in thetraffic line (See for example “Broadband vehicle-to-vehiclecommunication using an extended autonomous cruise control sensor,” ByHeddebaut et al, Published 17 May 2005). Some driverless cars mayutilize information available from any car in their vicinity, evennon-adjacent ones, but this is typically limited to alerts about roadconditions and traffic events occurring ahead.

FIG. 1 illustrates a convoy of motor-vehicles, where a vector of travel(i.e. direction, magnitude) is illustrated by arrows. In the example ofFIG. 1 all vehicles are travelling at the same speed and in the samedirection. In the example of FIG. 1, (i) vehicle 100B is behind andfollows vehicle 100A and (ii) vehicle 100C is behind and follows vehicle100B; (ii) vehicle 100D is behind and follows vehicle 100C.

FIGS. 2A-2B illustrate one example of an accident between motor-vehicles100A, 100B—FIG. 2A corresponds to an earlier moment in time before theaccident occurs and FIG. 2B illustrate the moment immediately after thecollision between motor-vehicles 100A, 100B.

FIGS. 3A-3D illustrate a very specific type of accident—a chainaccident. In FIG. 3A, first 100A and second 100B vehicles are stoppedand waiting at a stop sign as a third vehicle 100C approaches frombehind. In FIG. 3B, the third vehicle 100C hits the second vehicle 100Bfrom behind, imparting to the second vehicle 100B forward momentum(Illustrated in FIG. 3C). In FIG. 3D, as a result of this forwardmomentum, second vehicle 100B hits first vehicle 100A from behind.

Intoxicated Drivers

Intoxicated drivers, otherwise known as ‘drunk drivers,’ are a scourgeon our society. According to the National Highway Traffic SafetyAdministration, drunk driving involvement in fatal crashes in 2014 wasalmost four times higher at night than during the day (34 versus 9percent).

US 20140297111, incorporated herein by reference in its entirety,discloses a vehicle control system comprising: an alcohol detector whichdetects an alcohol intake level of a driver of a vehicle; and acontroller which controls the alcohol detector so that a detection ofthe alcohol intake level is started during a run of the vehicle justafter the vehicle starts up and initially moves, wherein the controllerdetermines whether the driver is a drunk person based on a detectionresult obtained from the alcohol detector, and wherein the controllerstops the vehicle when the controller determines that the driver is thedrunk person.

US 20140365142, incorporated herein by reference in its entirety,discloses wearable alcohol sensor that measures a user's blood alcohollevel by detecting an amount of alcohol in the user's insensibleperspiration.

SUMMARY

A method for attempting to avoid a potential motor-vehicle accidentand/or minimizing damage caused by the potential motor-vehicle accidentcomprises: a. wirelessly transmitting, by non-visual electro-magnetic(EM) radiation and from a first motor-vehicle, a first accident alertcomprising accident prediction data about a potential motor-vehicleaccident; b. receiving the first accident alert at a secondmotor-vehicle; c. in response to the receiving of the first accidentalert, wirelessly transmitting a second accident alert by non-visual EMradiation and from the second motor-vehicle; d. receiving the secondaccident alert by a third motor-vehicle; and e. in response to thereceiving of the second accident alert, attempting, by an onboardcomputer of the third motor-vehicle, (i) to avoid being involved in thepotential motor-vehicle accident and/or (ii) to reduce (e.g. minimize)damage inflicted upon the third motor-vehicle as a result of involvementin the potential motor-vehicle accident by performing at least onevehicle control action.

In some embodiments, the onboard computer of the third motor-vehicleperforms the at least one vehicle control action so as to attempt toavoid being involved in the potential motor-vehicle accident.

In some embodiments, the onboard computer of the third motor-vehicleperforms the at least one vehicle control action so as to attempt toreduce (e.g. minimize) damage inflicted upon the third motor-vehicle asa result of involvement in the potential motor-vehicle accident.

In some embodiments, in addition to accident prediction data, the firstaccident alert and/or the second accident alert includes factual inputdata.

In some embodiments, the factual input data includes at least one of ablood alcohol level of a human driver of the first motor vehicle and ablood alcohol level of a human driver of the second motor vehicle.

In some embodiments, the factual input data of the first and/or secondaccident alert includes at least one of: (i) an indication that thefirst motor-vehicle is braking; (ii) an indication that the firstmotor-vehicle is decelerating; (iii) an indication that the firstmotor-vehicle is accelerating; and (iv) an indication of an action by afourth motor-vehicle.

In some embodiments, the accident prediction data of the first and/orsecond accident alerts includes an indication that an accident mightoccur between the first motor-vehicle and the second motor-vehicle.

In some embodiments, the accident prediction data of the first and/orsecond accident alerts includes an indication that an accident mightoccur between the first motor-vehicle and a fourth motor-vehicle.

In some embodiments, the second motor-vehicle follows the firstmotor-vehicle and the third motor-vehicle follows the secondmotor-vehicle.

In some embodiments, the second motor-vehicle follows the thirdmotor-vehicle and the first motor-vehicle follows the secondmotor-vehicle.

In some embodiments, the attempting by the third motor-vehicle to avoidbeing involved in the potential motor-vehicle accident and/or tominimize damage comprises at least one of: (i) accelerating the thirdmotor-vehicle; (ii) decelerating the third motor-vehicle; (iii)employing a steering system of the third motor-vehicle; and (iv)employing a braking system of the third motor vehicle.

In some embodiments, the accident prediction data of the received firstaccident alert is evaluated at the second motor-vehicle and thetransmitting of the second accident alert from the second motor-vehicleis contingent upon results of the evaluation.

In some embodiments, (i) one or more onboard computer(s) of the firstmotor-vehicle computes accident prediction data of the first accidentalert from a first set of factual input data; and (ii) one or moreonboard computer(s) of the second motor-vehicle computes accidentprediction data of the second accident alert from a second set offactual input data that includes factual input data not present withinthe first set of factual input data.

In some embodiments, the factual input data included in the second setof factual input data and not present within the first set of factualinput data comprises a measurement of a blood alcohol level of a humandriver of the second motor-vehicle.

In some embodiments, an alcohol sensor is present in the second motorvehicle to measure the blood alcohol level of the human driver bydetecting an amount of alcohol in his/her perspiration.

In some embodiments, the accident prediction data of the second accidentalert is evaluated at the second motor-vehicle and the transmitting ofthe second accident alert from the second motor-vehicle is contingentupon results of the evaluation.

In some embodiments, onboard computer(s) of the second motor-vehiclederive(s) accident prediction data of the second accident alert onlyfrom accident prediction data of the received first accident alert.

In some embodiments, an onboard computer of the first motor-vehicleevaluates accident prediction data and only transmits the first accidentalert if a likelihood and/or severity of a predicted accident exceeds athreshold.

In some embodiments, each of the first, second and third motor-vehicleis a car.

A method for responding to a prediction of a potential accidentinvolving first, second and third motor-vehicles, with the first, secondand third motor-vehicles arranged so that (i) the second motor-vehicleis behind the first motor-vehicle and (ii) the first motor-vehicle isbehind the third motor-vehicle, the method comprising: a.computationally predicting an accident scenario by an onboard computerof a first motor-vehicle, the accident scenario indicating that thefirst motor-vehicle might be hit from behind by a second motor-vehicle;b. in response to the predicting, wirelessly transmitting, by non-visualEM radiation and from the first motor-vehicle, an accident alert; c.receiving the accident alert by a third motor-vehicle that is in frontof the first motor-vehicle; and d. in response to the receiving of theaccident alert, attempting, by an onboard computer of the thirdmotor-vehicle, (i) to avoid being hit from behind by the firstmotor-vehicle and/or (ii) to reduce (e.g. minimize) damage inflictedupon the third motor-vehicle resulting from being hit from behind by thefirst motor-vehicle, by performing at least one vehicle control action.

In some embodiments, the onboard computer of the third motor-vehicleperforms the at least one vehicle control action so as to attempt toavoid being hit from behind by the first motor-vehicle.

In some embodiments, the onboard computer of the third motor-vehicleperforms the at least one vehicle control action so as to attempt toreduce (e.g. minimize) damage inflicted upon the third motor-vehicleresulting from being hit from behind by the first motor-vehicle.

In some embodiments, the accident alert comprises an indication that thefirst motor-vehicle might be hit from behind by the secondmotor-vehicle.

In some embodiments, the accident alert comprises an indication that anaccident may occur between the first and third motor-vehicles.

In some embodiments, the at least one vehicle control action comprises avehicle control action that causes accelerating the third motor-vehicle.

In some embodiments, i. the onboard computer of the first and/or of thethird motor-vehicle predicts at least one parameter of a chain accidentresulting from said accident scenario in which the second motor-vehiclehits the first motor-vehicle and the first motor-vehicle hits the thirdmotor-vehicle; and ii. the at least one vehicle control action isselected in accordance with at least one of the at least one parameterof the chain accident.

A method for responding to a prediction of a potential accidentinvolving first, second and third motor-vehicles, the method comprising:a. computationally predicting an accident scenario by an onboardcomputer of the first motor-vehicle, the accident scenario indicatingthat a first motor-vehicle accident might occur between the first andsecond motor-vehicles; b. determining, by the onboard computer of thefirst motor-vehicle, if changing a velocity of the first motor-vehiclein order to (i) avoid the first motor-vehicle accident and/or (ii)reduce a likelihood thereof and/or (iii) reduce a severity thereof would(i) results in a second motor-vehicle accident between the first andthird motor-vehicles and/or (ii) increases a likelihood of the secondmotor-vehicle accident; and c. in response to a positive determining,performing at least one vehicle control action by the onboard computerof the first motor-vehicle for adjusting the velocity of the firstmotor-vehicle according to respective velocities and/or accelerations ofthe second and third motor-vehicles.

In some embodiments, the velocity of the first motor-vehicle is adjustedso as to reduce (e.g. minimize) a predicted amount of damage inflictedupon the first motor-vehicle as a result of its involvement in the firstand second motor-vehicle accidents.

In some embodiments, the velocity of the first motor-vehicle is adjustedso as to reduce (e.g. minimize) a predicted aggregate amount of damageinflicted upon a combination of at least two of the first, second andthird motor-vehicles as a result of their collective involvement in thefirst and/or second motor-vehicle accidents.

In some embodiments, the velocity of the first motor-vehicle is adjustedwithout attempting to avoid the first motor-vehicle accident.

In some embodiments, the first motor-vehicle follows the secondmotor-vehicle and the third motor-vehicle follows the firstmotor-vehicle.

In some embodiments, the first motor-vehicle follows the thirdmotor-vehicle and the second motor-vehicle follows the firstmotor-vehicle.

An anti-accident device for operation onboard a host motor-vehiclecomprises: a. a prediction-engine for processing factual input dataabout a plurality of motor-vehicles and computationally predicting anaccident scenario, thereby generating output prediction data of apotential accident; b. a wireless transmitter for wirelesslytransmitting non-visual EM signals; c. a wireless receiver forwirelessly receiving non-visual EM signals; and d. a device controllerfor sending control signals to onboard vehicle controls of the hostmotor-vehicle where the anti-accident device resides, wherein theanti-accident onboard device provides the following features: i. inresponse to a predicting, by the prediction engine, of an accidentscenario about a first potential motor-vehicle accident, the devicecontroller transmits, via the wireless transmitter, a first outgoingaccident alert comprising accident prediction data about the firstpotential motor-vehicle accident; ii. in response to a receiving, viathe wireless receiver, of a first incoming accident alert comprisingaccident prediction data about a second potential motor-vehicleaccident, the device controller transmits, via the wireless transmitter,a second outgoing accident alert comprising accident prediction data forthe second potential motor-vehicle accident; iii. in response to areceiving, via the wireless receiver, of a second incoming accidentalert comprising accident prediction data about a third potentialmotor-vehicle accident between two or more external motor-vehicles, thedevice controller sends control signals to one or more onboard vehiclecontrols of the host motor-vehicle so as (A) to avoid involvement, ofthe host motor-vehicle, in the third potential motor-vehicle accident;and/or (B) to reduce (e.g. minimize) damage inflicted upon the hostmotor-vehicle as a result of involvement in the third potentialmotor-vehicle accident by performing at least one vehicle controlaction.

An anti-accident device for operation onboard a host motor-vehiclecomprises: a. a prediction-engine for processing factual input dataabout a plurality of motor-vehicles and computationally predicting anaccident scenario; b. a wireless transmitter for wirelessly transmittingnon-visual EM signals; c. a wireless receiver for wirelessly receivingnon-visual EM signals; and d. a device controller for sending controlsignals to onboard vehicle controls of the host motor-vehicle where theanti-accident device resides, wherein the anti-accident onboard deviceprovides the following features: i. in response to a predicting by theprediction-engine that the host motor-vehicle might be hit from behindby a first external motor-vehicle, the device controller transmits anoutgoing accident alert via the wireless transmitter; ii. in response toan incoming accident alert that: A. is received via the wirelessreceiver; B. is received from a second external motor-vehicle that isbehind of the host motor-vehicle; and C. indicates that an accidentmight occur behind the host motor-vehicle where the second externalmotor-vehicle is hit from behind by a third external motor-vehicle, thedevice controller sends control signals to one or more onboard vehiclecontrols of the host motor-vehicle to perform at least one vehiclecontrol action in order to avoid the host motor-vehicle being hit frombehind by the second external motor-vehicle and/or in order to reduce(e.g. minimize) damage inflicted upon the host motor-vehicle resultingfrom being hit from behind by the second external motor-vehicle.

An anti-accident device for operation onboard a host motor-vehiclecomprises: a. a prediction-engine for: processing factual input dataabout a plurality of motor-vehicles and computationally predicting anaccident scenario indicating that a first motor-vehicle accident mayoccur between the host motor-vehicle and a first external motor-vehicle;and determining if changing a velocity of the host motor-vehicle inorder (i) to avoid the first motor-vehicle accident and/or (ii) toreduce a likelihood thereof and/or (iii) to reduce a severity thereof,would result in one or more of: (A) a second motor-vehicle accidentoccurring between the host motor-vehicle and a second externalmotor-vehicle and (ii) an increase in a likelihood that the secondmotor-vehicle accident will occur; and b. a device controller forresponding to a positive determining by sending control signals to oneor more onboard vehicle controls of the host motor-vehicle to adjust thevelocity of the host motor-vehicle according to respective velocitiesand/or accelerations of the first and second external motor-vehicles.

A method for alerting a car about a potential car accident, comprises:a. transmitting, by a first car, a first accident alert; b. receivingthe first accident alert by a second car; c. In response to thereceiving of the first accident alert, transmitting a second accidentalert by the second car; d. receiving the second accident alert by athird car; and e. in response to the receiving of the second accidentalert, attempting to avoid a car accident by the third car.

In some embodiments, the first accident alert comprises an indicationthat the first car is braking.

In some embodiments, the first accident alert comprises an indicationthat the first car is decelerating.

In some embodiments, the first accident alert comprises an indicationthat the first car is accelerating.

In some embodiments, the first accident alert comprises an indication ofan action by a fourth car.

In some embodiments, the first accident alert comprises an indicationthat a car accident might occur between the first car and the secondcar.

In some embodiments, the first accident alert comprises an indicationthat a car accident might occur between the first car and a fourth car.

In some embodiments, the second accident alert comprises an indicationthat the first car is braking.

In some embodiments, the second accident alert comprises an indicationthat the first car is decelerating.

In some embodiments, the second accident alert comprises an indicationthat the first car is accelerating.

In some embodiments, the second accident alert comprises an indicationof an action by a fourth car.

In some embodiments, the second accident alert comprises an indicationthat a car accident might occur between the first car and the secondcar.

In some embodiments, the second accident alert comprises an indicationthat a car accident might occur between the first car and a fourth car.

In some embodiments, the second car follows the first car and the thirdcar follows the second car.

In some embodiments, the second car follows the third car and the firstcar follows the second car.

In some embodiments, the attempting to avoid a car accident comprisesbraking by the third car.

In some embodiments, the attempting to avoid a car accident comprisesdecelerating by the third car.

In some embodiments, the attempting to avoid a car accident comprisesaccelerating by the third car.

A method for alerting a car about a potential car accident, comprises:a.

determining, by a first car, that a car accident might occur between thefirst car and a second car with the second car hitting the first carfrom behind; b. transmitting, by the first car, an accident alert; c.receiving the accident alert by a third car which is in front of thefirst car; d. in response to the receiving of the accident alert,attempting to avoid a car accident by the third car.

In some embodiments, the accident alert comprises an indication that thefirst car might be hit by the second car from behind.

In some embodiments, the accident alert comprises an indication that acar accident might occur between the first car and the third car.

In some embodiments, the attempting to avoid a car accident comprisesaccelerating by the third car.

A method for alerting a car about a potential car accident comprises: a.

determining, by a first car, that a first car accident might occurbetween the first car and a second car; b. determining, by the firstcar, that changing its speed in order to avoid the first car accidentwith the second car would result in the first car having a second caraccident with a third car; c. in response to the determining, adjustingthe speed of the first car according to the speed of the second car andaccording to the speed of the third car.

In some embodiments, the adjusted speed of the first car is selected soas to reduce the amount of an overall damage suffered by the first carfrom the first car accident and the second car accident.

In some embodiments, the first car follows the second car and the thirdcar follows the first car.

In some embodiments, the first car follows the third car and the secondcar follows the first car.

A method for attempting at least one of avoiding a motor-vehicleaccident and minimizing damage caused by the motor-vehicle accident, themethod comprising: a. wirelessly transmitting, by non-visualelectromagnetic (EM) radiation and from a first motor-vehicle, a firstaccident alert comprising accident prediction data (i) containing aprediction that a motor-vehicle accident will occur and (ii) includingone or more predicted parameters of the motor-vehicle accident that ispredicted to occur; b. receiving the first accident alert at a secondmotor-vehicle; c. in response to the receiving of the first accidentalert, wirelessly transmitting a second accident alert by non-visual EMradiation and from the second motor-vehicle; d. receiving the secondaccident alert by a third motor-vehicle; and e. in response to thereceiving of the second accident alert, performing by an onboardcomputer of the third motor-vehicle at least one vehicle control actionso as to attempt at least one of the following: (i) avoiding beinginvolved in the motor-vehicle accident that is predicted to occur; and(ii) reducing damage inflicted upon the third motor-vehicle as a resultof involvement in the motor-vehicle accident that is predicted to occur.

In some embodiments, accident prediction data of the second accidentalert that is received by the third motor-vehicle (i) contains theprediction that the motor-vehicle accident will occur and (ii) includesone or more of the predicted parameters of the motor-vehicle accidentthat is predicted to occur.

In some embodiments, at least one of the first accident alert and thesecond accident alert includes factual input data in addition toaccident prediction data.

In some embodiments, the factual input data includes at least one of ablood alcohol level of a human driver of the first motor vehicle and ablood alcohol level of a human driver of the second motor vehicle.

In some embodiments, the factual input data includes at least one of:(i) an indication that the first motor-vehicle is braking; (ii) anindication that the first motor-vehicle is decelerating; (iii) anindication that the first motor-vehicle is accelerating; and (iv) anindication of an action by a fourth motor-vehicle.

In some embodiments, the accident prediction data includes an indicationthat an accident might occur between the first motor-vehicle and thesecond motor-vehicle.

In some embodiments, the accident prediction data includes an indicationthat an accident might occur between the first motor-vehicle and afourth motor-vehicle.

In some embodiments, the second motor-vehicle follows the firstmotor-vehicle and the third motor-vehicle follows the secondmotor-vehicle.

In some embodiments, the second motor-vehicle follows the thirdmotor-vehicle and the first motor-vehicle follows the secondmotor-vehicle.

In some embodiments, the at least one vehicle control action performedby the onboard computer of the third motor-vehicle includes at least oneof the following: (i) accelerating the third motor-vehicle; (ii)decelerating the third motor-vehicle; (iii) employing a steering systemof the third motor-vehicle; and (iv) employing a braking system of thethird motor vehicle.

In some embodiments, the accident prediction data of the received firstaccident alert is evaluated at the second motor-vehicle and thetransmitting of the second accident alert from the second motor-vehicleis contingent upon results of the evaluation.

In some embodiments, (i) one or more onboard computer(s) of the firstmotor-vehicle computes the accident prediction data of the firstaccident alert from a first set of factual input data; and (ii) one ormore onboard computer(s) of the second motor-vehicle computes accidentprediction data of the second accident alert from a second set offactual input data that includes factual input data not present withinthe first set of factual input data.

In some embodiments, the factual input data included in the second setof factual input data and not present within the first set of factualinput data comprises a measurement of a blood alcohol level of a humandriver of the second motor-vehicle.

In some embodiments, an alcohol sensor is present in the second motorvehicle to measure the blood alcohol level of the human driver bydetecting an amount of alcohol in his/her perspiration.

In some embodiments, accident prediction data of the second accidentalert is evaluated at the second motor-vehicle and the transmitting ofthe second accident alert from the second motor-vehicle is contingentupon results of the evaluation.

In some embodiments, onboard computer(s) of the second motor-vehiclederive(s) accident prediction data of the second accident alert onlyfrom the accident prediction data of the received first accident alert.

In some embodiments, an onboard computer of the first motor-vehicleevaluates accident prediction data and only transmits the first accidentalert if at least one of a likelihood of a predicted accident andseverity thereof exceeds a threshold.

A method for handling a prediction that a first motor-vehicle accidentinvolving first and second motor-vehicles will occur, the methodcomprising: a. operating an onboard computer of the first motor-vehicleto predict that the first motor-vehicle accident between the first andsecond motor-vehicles will occur; b. determining, by the onboardcomputer of the first motor-vehicle, if changing a velocity of the firstmotor-vehicle in order to achieve at least one of the following: (i)avoid the first motor-vehicle accident, (ii) reduce a likelihoodthereof, and (iii) reduce a severity thereof, would result in one ormore of: A. a second motor-vehicle accident occurring between the firstmotor-vehicle and a third motor-vehicle; and B. an increase in alikelihood that the second motor-vehicle accident will occur; and c. inresponse to a positive determining, performing at least one vehiclecontrol action by the onboard computer of the first motor-vehicle foradjusting the velocity of the first motor-vehicle according to at leastone of: i. respective velocities of the second and third motor-vehicles;and ii. respective accelerations of the second and third motor vehicles.

In some embodiments, the velocity of the first motor-vehicle is adjustedso as to reduce a predicted amount of damage inflicted upon the firstmotor-vehicle as a result of its involvement in the first and secondmotor-vehicle accidents.

In some embodiments, the velocity of the first motor-vehicle is adjustedso as to reduce a predicted aggregate amount of damage inflicted upon acombination of at least two of the first, second and thirdmotor-vehicles as a result of their collective involvement in the firstand second motor-vehicle accidents.

In some embodiments, the velocity of the first motor-vehicle is adjustedwithout attempting to avoid the first motor-vehicle accident.

In some embodiments, the first motor-vehicle follows the secondmotor-vehicle and the third motor-vehicle follows the firstmotor-vehicle.

In some embodiments, the first motor-vehicle follows the thirdmotor-vehicle and the second motor-vehicle follows the firstmotor-vehicle.

An anti-accident device for operation onboard a host motor-vehicle, theanti-accident device comprising: a. a prediction-engine for processingfactual input data about a plurality of motor-vehicles andcomputationally predicting an accident scenario, thereby generatingaccident prediction data; b. a wireless transmitter for wirelesslytransmitting non-visual electromagnetic (EM) signals; c. a wirelessreceiver for wirelessly receiving non-visual EM signals; and d. a devicecontroller for sending control signals to onboard vehicle controls ofthe host motor-vehicle where the anti-accident device resides, whereinthe anti-accident onboard device provides the following features: i. inresponse to a predicting, by the prediction engine, of an accidentscenario about a first motor-vehicle accident, the device controllertransmits, via the wireless transmitter, a first outgoing accident alertcomprising a prediction that the first motor-vehicle accident will occurand one or more predicted parameters of the first motor-vehicle accidentthat is predicted to occur; ii. in response to a receiving, via thewireless receiver, of a first incoming accident alert comprisingaccident prediction data about a second motor-vehicle accident, thedevice controller transmits, via the wireless transmitter, a secondoutgoing accident alert comprising accident prediction data for thesecond motor-vehicle accident; iii. in response to a receiving, via thewireless receiver, of a second incoming accident alert comprisingaccident prediction data about a third motor-vehicle accident betweentwo or more external motor-vehicles, the device controller sends controlsignals to one or more onboard vehicle controls of the hostmotor-vehicle to perform at least one vehicle control action, so as toattempt at least one of the following: (A) avoiding involvement, of thehost motor-vehicle, in the third motor-vehicle accident; and (B)reducing damage inflicted upon the host motor-vehicle as a result ofinvolvement in the third motor-vehicle accident.

In some embodiments, the anti-accident onboard device is configured sothat the second outgoing accident alert (i) contains the prediction thatthe second motor-vehicle accident will occur and (ii) includes one ormore parameters of the second motor-vehicle accident.

An anti-accident device for operation onboard a host motor-vehicle, theanti-accident device comprising: a. a prediction-engine for processingfactual input data about a plurality of motor-vehicles andcomputationally predicting future occurrences of motor-vehicle accidentsas well as one or more parameters of the motor-vehicle accidents thatare predicted to occur; b. a wireless transmitter for wirelesslytransmitting non-visual electromagnetic (EM) signals; c. a wirelessreceiver for wirelessly receiving non-visual EM signals; and d. a devicecontroller for sending control signals to onboard vehicle controls ofthe host motor-vehicle where the anti-accident device resides, whereinthe anti-accident onboard device provides the following features: i. inresponse to a computed prediction by the prediction-engine that a firstmotor-vehicle accident will occur, where the host motor-vehicle will behit from behind by a first external motor-vehicle, the device controllertransmits an outgoing accident alert via the wireless transmitter wherethe outgoing accident alert comprises: A. the prediction that the firstmotor-vehicle accident will occur as computed by the prediction-engine;and B. one or more computationally predicted parameters of the firstmotor-vehicle accident that is predicted to occur as computed by theprediction-engine; ii. in response to an incoming accident alert that:A. is received via the wireless receiver; B. is received from a secondexternal motor-vehicle that is behind of the host motor-vehicle; and C.indicates that a second motor-vehicle accident will occur behind thehost motor-vehicle where the second external motor-vehicle is hit frombehind by a third external motor-vehicle; D. includes one or moreparameters of the second motor-vehicle accident, the device controllersends control signals to one or more onboard vehicle controls of thehost motor-vehicle to perform at least one vehicle control action so asto attempt at least one of the following: A. avoiding the hostmotor-vehicle being hit from behind by the second externalmotor-vehicle; and B. reducing damage inflicted upon the hostmotor-vehicle resulting from being hit from behind by the secondexternal motor-vehicle.

In some embodiments, the outgoing accident alert transmitted via thewireless transmitter of the host motor-vehicle comprises an indicationthat the host motor-vehicle will be hit from behind by the firstexternal motor-vehicle.

In some embodiments, the outgoing accident alert transmitted via thewireless transmitter of the host motor-vehicle comprises an indicationthat an accident may occur between the host motor-vehicle and a fourthexternal motor-vehicle.

In some embodiments, the at least one vehicle control action includes avehicle control action that causes accelerating of the hostmotor-vehicle.

In some embodiments, the vehicle control action that causes acceleratingof the host motor-vehicle attempts to avoid being hit from behind by thesecond external motor-vehicle.

In some embodiments, the vehicle control action that causes acceleratingof the host motor-vehicle attempts to reduce damage inflicted upon thehost motor-vehicle resulting from being hit from behind by the secondexternal motor-vehicle.

An anti-accident device for operation onboard a host motor-vehicle, theanti-accident device comprising: a. a prediction-engine for: processingfactual input data about a plurality of motor-vehicles andcomputationally predicting that a first motor-vehicle accident betweenthe host motor-vehicle and a first external motor-vehicle will occur;and determining if changing a velocity of the host motor-vehicle inorder to achieve at least one of the following: (i) to avoid the firstmotor-vehicle accident, (ii) to reduce a likelihood thereof, (iii) toreduce a severity thereof, would result in one or more of: (A) a secondmotor-vehicle accident occurring between the host motor-vehicle and asecond external motor-vehicle and (B) an increase in a likelihood thatthe second motor-vehicle accident will occur; and b. a device controllerfor responding to a positive determining by sending control signals toone or more onboard vehicle controls of the host motor-vehicle to adjustthe velocity of the host motor-vehicle according to at least one ofrespective velocities of the first and second external motor-vehiclesand respective accelerations of the first and second externalmotor-vehicles.

An anti-accident system comprising: a plurality of anti-accidentdevices, each given anti-accident device of the plurality respectivelycomprising: a. a respective prediction-engine for processing factualinput data about a plurality of motor-vehicles and computationallypredicting future occurrences of motor-vehicle accidents as well as oneor more parameters of the motor-vehicle accidents that are predicted tooccur; b. a respective wireless transmitter for wirelessly transmittingnon-visual electromagnetic (EM) signals; c. a respective wirelessreceiver for wirelessly receiving non-visual EM signals; and d. arespective device controller for sending control signals to onboardvehicle controls of a respective host motor-vehicle where the givenanti-accident device resides, wherein the plurality of anti-accidentdevices comprises first, second and third anti-accident devices suchthat, when the first, second and third anti-accident devicesrespectively reside in first, second and third motor-vehicles, theanti-accident devices perform the following operations: i. theprediction engine of the first anti-accident device predicts that aspecific motor vehicle accident will occur and computes one or morecomputationally predicted parameters of the specific motor vehicleaccident predicted to occur; ii. the wireless transmitter of the firstanti-accident device wirelessly transmits, by non-visual electromagnetic(EM) radiation and from the first motor-vehicle, a first accident alertcomprising the prediction that the specific motor vehicle accident willoccur along with one or more of the computationally predicted parametersof the specific motor-vehicle accident that is predicted to occur; ii.the second anti-accident device wirelessly receives the first accidentalert, and responds by wirelessly transmitting a second accident alertby non-visual EM radiation; iii. the third anti-accident devicewirelessly receives the second accident alert and responds by performingat least one vehicle control action so as to attempt at least one of thefollowing: (A) avoiding getting the third motor-vehicle involved in thespecific motor-vehicle accident that is predicted to occur and (B)reducing damage inflicted upon the third motor-vehicle as a result ofinvolvement in the specific motor-vehicle accident that is predicted tooccur.

In some embodiments, the second accident alert wirelessly transmitted bythe second anti-accident device comprises the prediction that thespecific motor vehicle accident will occur along with one or more of thecomputationally predicted parameters of the specific motor-vehicleaccident that is predicted to occur.

An anti-accident system comprising: a plurality of anti-accidentdevices, each given anti-accident device of the plurality respectivelycomprising: a. a respective prediction-engine for processing factualinput data about a plurality of motor-vehicles and computationallypredicting future occurrences of motor-vehicle accidents as well as oneor more parameters of the motor-vehicle accidents that are predicted tooccur; b. a respective wireless transmitter for wirelessly transmittingnon-visual electromagnetic (EM) signals; c. a respective wirelessreceiver for wirelessly receiving non-visual EM signals; and d. arespective device controller for sending control signals to onboardvehicle controls of a respective host motor-vehicle where the givenanti-accident device resides, wherein the plurality of anti-accidentdevices comprises first and second anti-accident devices such that,when: i. first, second and third motor-vehicles are arranged relative toeach other so that the second motor-vehicle is behind the firstmotor-vehicle and the first motor-vehicle is behind the thirdmotor-vehicle; and ii. the first anti-accident device resides in thefirst motor-vehicle and the second anti-accident device resides in thethird motor-vehicle, the first and second anti-accident devices performthe following operations: A. in response to a prediction-engine of thefirst anti-accident device computationally predicting that a specificmotor-vehicle accident will occur where the first motor-vehicle will behit from behind by the second motor-vehicle along with one or moreparameters of the specific motor-vehicle accident that is predicted tooccur, a wireless transmitter of the first anti-accident devicewirelessly transmits, by non-visual EM radiation and from the firstmotor-vehicle, an accident alert comprising the prediction that thespecific motor-vehicle accident will occur and the predicted one or moreparameters of the specific motor-vehicle accident; and B. in response toa wireless receiving of the accident alert by the second anti-accidentdevice on the third motor-vehicle which is in front of the firstmotor-vehicle, the second anti-accident device performs at least onevehicle control action at the third motor-vehicle so as to attempt atleast one of the following (i) avoiding being hit from behind by thefirst motor-vehicle and (ii) reducing damage inflicted upon the thirdmotor-vehicle resulting from being hit from behind by the firstmotor-vehicle.

In some embodiments, the accident alert transmitted by the wirelesstransmitter of the first motor-vehicle comprises an indication that thefirst motor-vehicle will be hit from behind by the second motor-vehicle.

In some embodiments, the accident alert transmitted by the wirelesstransmitter of the first motor-vehicle comprises an indication that anaccident may occur between the first and third motor-vehicles.

In some embodiments, the at least one vehicle control action includes avehicle control action that causes accelerating of the thirdmotor-vehicle.

In some embodiments, the vehicle control action that causes acceleratingof the third motor-vehicle attempts to avoid being hit from behind bythe first motor-vehicle.

In some embodiments, the vehicle control action that causes acceleratingof the third motor-vehicle attempts to reduce damage inflicted upon thethird motor-vehicle resulting from being hit from behind by the firstmotor-vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1, 2A-2B and 3A-3D present some prior-art accident scenarios.

FIGS. 4, 7A-7B, 9 and 11 are flow-charts of methods that are performedby driverless vehicles that may reduce a likelihood and/or severity ofmotor-vehicle accident(s).

FIG. 5 is a block diagram of an anti-accident device according to someembodiments.

FIGS. 6A-6E, 8A-8E, 10A-10E and 12 illustrate various use-casesaccording to some embodiments of the invention.

DETAILED DESCRIPTION OF EMBODIMENTS

The claims below will be better understood by referring to the presentdetailed description of example embodiments with reference to thefigures. The description, embodiments and figures are not to be taken aslimiting the scope of the claims. It should be understood that not everyfeature of the presently disclosed methods, apparatuses, and computerreadable media having stored thereon computer code for attempting toavoid potential motor-vehicle accident(s) is necessary in everyimplementation. It should also be understood that throughout thisdisclosure, where a process or method is shown or described, the stepsof the method may be performed in any order or simultaneously, unless itis clear from the context that one step depends on another beingperformed first. As used throughout this application, the word “may” isused in a permissive sense (i.e., meaning “having the potential to’),rather than the mandatory sense (i.e. meaning “must”).

Definitions

Within this application the following terms should be understood to havethe following meaning:

a. motor-vehicle—a wheeled or tracked motorized vehicle that travels onland including car, motorcycle, truck, bus, and van. Typically, but notnecessarily, a motor-vehicle has different compartments including (i) acabin for passengers (ii) an engine compartment where the engine islocated (e.g. under the hood in front of the cabin) and (iii) a baggagecompartment or trunk. An engine of a car may be of any type, includingan internal combustion motor and an electrical motor.b. onboard device of a motor-vehicle—a device mounted to and/or disposedon and/or disposed within and/or attached to a motor-vehicle. Thismotor-vehicle is referred to as the ‘host’ motor-vehicle of the onboarddevice.An onboard device of a host motor-vehicle necessarily travels with themotor-vehicle as it moves—e.g. at the same velocity or substantially thesame velocity. An onboard device is not required to be permanentlyassociated with a motor-vehicle—in some embodiments, an onboard devicemay be temporarily associated with a motor-vehicle (e.g. a passengermanually brings a smartphone into the cabin of the car—for example, inhis/her pocket). In other embodiments, an onboard device may bepermanently associated with a motor-vehicle—i.e. a passenger cannotsimply remove the device from the motor-vehicle without using a specialtool. In different embodiments, an onboard device may be disposed withinthe cabin, the engine compartment (e.g. attached to the engine or to acomponent thereof), under the chassis, on the roof, or in the baggagecompartment of the motor-vehicle. Onboard devices may be mechanical,electronic or combinations thereof.Examples of an onboard device of a motor-vehicle include (i) onboardelectronic circuitry (e.g. any combination of hardware and/orsoftware—e.g. digital computer or code/software executing on a digitalcomputer), (ii) an onboard vehicle control (defined below), (iii) anonboard sensor (e.g. radar device, camera, etc.), (iv) an onboardtransmitter or receiver of EM radiation (e.g. transmitter or receiver ofnon-visible EM radiation); and (v) an onboard passenger-safety devicesuch as a seatbelt or an airbag.For the present disclosure, an onboard device is said to reside (i.e.temporally or permanently) on (or to reside in) a host motor-vehicle.For the onboard device, all motor-vehicles other than the hostmotor-vehicle are referred to as ‘external’ motor-vehicles.c. velocity—Refers to a vector, having both magnitude and orientation.In other words, the velocity of a motor-vehicle includes both its speed(for example measured in meters per second) and its direction in space(for example expressed by a horizontal direction measured in degreesfrom north or from the centerline of the road, or expressed by both ahorizontal direction and a vertical direction measured in degrees fromthe horizontal plane).d. onboard vehicle control—a specific type of an onboard device of amotor-vehicle—an onboard vehicle control may have any feature(s) ofonboard devices of a motor-vehicle. Operations of an onboard vehiclecontrol affect the motion of the vehicle. An onboard vehicle control maybe situated in the cabin such as an accelerator pedal (‘gas’), adecelerator pedal (‘brake’), and a steering device (e.g. steeringwheel). Alternatively, an onboard vehicle control may be outside of thecabin—for example, within the engine (e.g. a valve within the enginewhich regulates a flow of fuel), mounted to and under the vehicle, or inany other location. A device that is outside of the vehicle and notonboard, but which nevertheless sends signals that control the vehicle(e.g. a wireless transmitter which wirelessly sends signals to onboardvehicle control(s)) is not considered an onboard vehicle control, evenif the wireless signals (i.e. once received at the vehicle and processedby onboard vehicle control(s)) affect the motion of the vehicle.In different embodiments, the onboard vehicle control is mechanicallycoupled to an element of the engine or of the transmission system or ofthe braking system or of the steering system, and/or an electricalcomponent of the onboard vehicle control is in wired or wirelesscommunication with an element mechanically coupled to an element of theengine or of the transmission system or of the braking system or of thesteering system.e. vehicle-control action—an action (e.g. triggered by an autonomousdriving module) that changes a physical state of an onboard vehiclecontrol to modify the motion of the motor-vehicle—e.g. depressing on thebrake pedal, releasing the break pedal, turning the steering wheel,increasing a flow of fuel to the engine, engaging brake discs to retardmotion of a wheel shaft. In some examples, a vehicle-control actionchanges a state of cabin-disposed vehicle control(s)—e.g. depressing thebrake pedal or turning the steering wheel. However, this is not arequirement, and in other examples a vehicle-control action only changesthe state of onboard vehicle control(s) disposed outside of the cabin(e.g. changing the state of a valve disposed within the enginecompartment).f. performing an action by a motor-vehicle—when a motor-vehicle or anonboard device thereof (e.g. an onboard vehicle control or an onboardtransmitter or an onboard receiver or onboard electric circuitry)automatically (i.e. without human intervention) performs one or more ofthe following: (i) performing a vehicle-control action (e.g. rotatingthe steering wheel)—for example, for a specific purpose such as avoidingan accident (e.g. either involving the given motor-vehicle or involvingtwo or more motor-vehicles other than the given motor-vehicle); (ii)transmitting or receiving a message (e.g. by transmitting or receivingnon-visible EM radiation)—this may include electronically encoding ordecoding the messageg. prediction engine—an electronic module (i.e. implemented in anycombination of hardware, firmware, software) which processes factualinput data and computationally predicts an outcome. The predictionengine may be deterministic, non-deterministic or a combination ofmodules of both types. In some embodiments, the prediction engineemploys machine learning algorithms including but not limited toregression models (e.g. linear or non-linear), Markov models, neuralnetworks, and the like. A deterministic prediction engine and adeterministic module always produce the same results for the same set ofinputs. A non-deterministic prediction engine and a non-deterministicmodule employ random or pseudo-random techniques (e.g. employ a randomor pseudo-random number generator) and thus do not always produce thesame results for the same set of inputs.h. factual input data—facts that are input into a prediction engine andmay be used by it for making its predictions. Examples of factual inputdata are:

(i) object-intrinsic data:

-   -   (A) motor-vehicle intrinsic data related to one or more        motor-vehicles: the weight of a motor-vehicle, the acceleration        capability, the braking capability, etc.;    -   (B) road intrinsic data—road curvature, road width, road slope,        road surface-status (e.g. asphalt vs. gravel)    -   (C) driver intrinsic data—age of the driver, number of years of        experience, driving record (i.e. is this a driver with many        previous accidents or traffic-violations?)

(ii) current-status data:

-   -   (A) Motor-vehicle data—absolute or relative location data        describing a position and/or orientation of vehicle(s); first        and/or second time derivatives of location data (e.g. speed or        acceleration, linear and/or angular); current or past status of        vehicle controls (e.g. brake-pedal currently depressed, angle of        the steering wheel);    -   (B) Terrain/road data—road conditions such as wet/dry/icy        pavement, ambient wind speed, ambient light/darkness, current        precipitation or lack thereof;    -   (C) Driver data—is the driver of vehicle X a human driver or an        automated computer-driving-module? If the driver is a human        driver, is this driver nervous or sleepy or intoxicated?        At least some factual input data can also be classified based        upon the instrument (i.e. type of instrument or location of        instrument) which acquires the factual input data. Some examples        of instrument-related categories of factual input data are:    -   (A) Radar-generated data    -   (B) Infrared data    -   (C) Camera-Acquired Data;    -   (D) Data acquired by an instrument mounted to a motor-vehicle;    -   (E) data acquired by an instrument not mounted to any        motor-vehicle—e.g. a stationary instrument (for example, mounted        to a street-sign)        i. computationally predicting an outcome—predicting, by a        prediction engine, at least one of (i) a future outcome (for        example, “car X will hit car Y”); (ii) a likelihood of a future        outcome, either absolute likelihood or relative likelihood        relative to another candidate future outcome (for example “there        is 80% probability that car X will hit car Y” or “the        probability that car X will hit car Y is twice the probability        that car X will hit car Z”). The prediction is based at least on        factual input data describing current and/or past facts. The        result of predicting an outcome is ‘outcome prediction data.’        Factual input data is by definition not ‘outcome prediction        data’ since ‘factual input data’ only refers to current or past        facts and do not involve any future prediction. In some        embodiments, ‘predicting an outcome’ includes predicting one or        more future vehicle control actions—e.g. predicting that the        driver of car X will notice that s/he is about to hit a tree so        s/he will swerve to the left. Predicting an outcome may be        related to a specific time-interval—i.e. the predicted outcome        may be a list of one or more event(s) that are predicted to        occur during a specific time interval.        j. updating an outcome prediction—a special case of        computationally predicting an outcome for a future time (or a        future time-interval) where there was a previous computation of        an outcome prediction related to the same future time (or future        time-interval) and at least a part of the factual input data        available to the current computation was not available for the        computation of the previous outcome prediction. It should be        noted that the current computation of the outcome prediction may        or may not rely on the previous outcome prediction. In other        words—the current computation may calculate the new outcome        prediction relying only on the current factual input data, or it        may save computation effort by using the previous outcome        prediction.        One example of updating an outcome prediction is as follows: Car        B follows Car A and Car A suddenly starts braking. At time t1,        it is predicted in PREDICTION_A that the likelihood of an        accident (car B hitting a rear of car A) occurring during the        time interval [t4,t5] between Car ‘A’ and Car ‘B’ is 80%. At        time t2, Car A reduces its braking force and Car B starts        braking. At time t3, based upon the updated data and        PREDICTION_A, it is predicted in PREDICTION_B that the        likelihood of an accident occurring during the time interval        [t4,t5] between Car ‘A’ and Car ‘B’ is now 40%. It is noted that        PREDICTION_B is based upon additional factual input data not        available to PREDICTION_A.        k. computationally predicting an accident scenario—a special        case of computationally predicting an outcome where information        about a ‘hypothetical accident’ (i.e. an accident which did not        occur yet at the time of the predicting and which may or may not        occur) is predicted. Predicting an accident scenario may refer        to at least one of: (i) predicting a likelihood of an        hypothetical accident (including as special cases predicting        that the accident is highly likely to occur and predicting that        the accident is highly unlikely to occur); (ii) predicting one        or more parameters of the hypothetical accident, which may be        for example (A) which motor-vehicles will collide in the        accident; (B) a ‘collision location’ (e.g. front left fender,        rear bumper, right door) of one or more motor-vehicles involved        in the accident; (C) a severity of the accident. The result of        predicting an accident scenario is the ‘accident prediction        data.’        In some embodiments, ‘computationally predicting an accident        scenario’ includes predicting one or more future vehicle control        actions and the ‘accident prediction data’ is generated in        accordance with the predicting of the one or more future vehicle        control actions. In other words, the predicting of an accident        scenario may predict that the present factual input data (for        example the cars' speed) will remain unchanged or that one or        more vehicle control actions will soon take place and affect the        accident prediction data.        l. accident prediction data—a special case of outcome prediction        data that is generated by computationally predicting an accident        scenario.        m. potential accident or hypothetical accident—For the present        disclosure, the terms “hypothetical accident” and “potential        accident” are used interchangeably and both refer to an accident        which has not yet occurred at the time of the predicting and        which may or may not occur.        n. accident alert—a message sent from a motor-vehicle that        includes at least ‘accident prediction data’. Optionally, in        addition to the ‘accident prediction data,’ the accident alert        also includes additional data such as ‘factual input data’ used        by the sending motor-vehicle for generating the accident        prediction data. One type of accident alert is an ‘elevated-risk        accident alert’ where the ‘accident prediction data’ included in        the message indicates that the likelihood or severity of a        hypothetical accident (i) exceeds a likelihood threshold or a        severity threshold or (ii) has increased relative to a previous        prediction, with or without expressly including a level of        likelihood and/or a level of severity. It should be understood        that the accident alert is not the accident prediction data        contained in the message but the event of sending the message.        In other words—if the same accident prediction data is sent in        two different messages then there are two different accident        alerts being sent.        o. evaluating accident prediction data—analyzing accident        prediction data to determine at least one of (i) if a likelihood        of an accident occurring (i.e. as described by the accident        prediction data) exceeds a threshold likelihood value; (ii) if a        severity of a predicted accident (i.e. as described by the        accident prediction data) exceeds a threshold severity value.        p. transmitting—wirelessly transmitting, typically by non-visual        EM radiation such as radio frequency (RF) radiation or infrared        (IR) radiation. Transmitting typically entails encoding data by        a digital computer or encoder.        q. motor-vehicle accident—a collision between multiple        motor-vehicles or a collision between a motor-vehicle and one or        more objects that are not motor-vehicles. When a given        motor-vehicle is involved in a motor-vehicle accident, at least        one other motor-vehicle hits the given motor-vehicle or the        given motor-vehicle hits at least one other motor-vehicle and/or        at least one non-motor-vehicle object.        r. attempting to avoid being involved in a potential        motor-vehicle accident—performing a vehicle-control action by a        given motor-vehicle (i.e. by sending electrical signals to        onboard vehicle control(s) of the given motor-vehicle by an        onboard computer of the given motor-vehicle) in response to an        accident alert. The accident alert includes accident prediction        data indicating that (i) a potential motor-vehicle accident may        occur (i.e. with non-zero probability); and (ii) there is a        non-zero probability that the given motor-vehicle will be        involved in that motor-vehicle accident.        In attempting to avoid being involved in this potential        motor-vehicle accident, the given motor-vehicle performs a        vehicle-control action that attempts to (i) prevent the        potential motor-vehicle accident from occurring altogether (or        at least, to reduce the probability that the potential        motor-vehicle accident will occur); or (ii) without necessarily        reducing the probability that the potential motor-vehicle        accident will occur, reduce the likelihood that the given        motor-vehicle will be involved in the potential motor-vehicle        accident (if it occurs).        Consider a predicted motor-vehicle accident where cars A, B, and        C are travelling as a convoy—i.e. car A is close behind car B,        and car B is close behind car C. In this predicted motor-vehicle        accident example, (i) car “A” is predicted to hit car “B” and to        transfer momentum to car “B”; and (ii) this transferred momentum        is predicted to cause car “B” to hit car “C.” Car C may attempt        to avoid being involved in the motor-vehicle accident by        accelerating—this might not necessarily reduce the probability        that some sort of accident will occur (i.e. car A hitting car B        without any other car getting hit)—however, this will reduce the        likelihood that car C will be hit by car B, and thus will reduce        the likelihood that car C will be involved in this potential        motor-vehicle accident.        Examples of attempts to avoid being involved in a potential        motor-vehicle accidents may include employing the steering        system to change a direction of travel in order to attempt to        avoid hitting an object in front of the motor-vehicle, employing        the braking system to slow the motor-vehicle in order to attempt        to avoid hitting another motor-vehicle, increasing the        power-output of the engine to increase the speed of a        motor-vehicle in order to attempt to avoid being hit by another        motor-vehicle (e.g. by engaging a throttle).        s. speed of a motor-vehicle—defined according to the direction        the motor-vehicle is facing. A motor-vehicle moving forward has        a positive speed, and a motor-vehicle moving backwards (e.g.        driving in reverse gear) has a negative speed.        t. accelerating a motor-vehicle—applying a positive acceleration        to the motor-vehicle so that its speed increases.        u. decelerating a motor-vehicle—applying a negative acceleration        to the motor-vehicle so that its speed decreases.        v. chain accident—an accident involving 3 or more        motor-vehicles, in which a first car hits a second car from        behind, and as a result of the collision between the first and        second cars, the second car subsequently hits a third car from        behind.        w. parameter of an accident—a property of an actual or potential        accident. Parameters could include a collision speed, an elapsed        time between a first and a second collision of a chain accident,        a collision angle, and a location on the road where the accident        would occur. A parameter of a potential chain accident is a        parameter that is predicted (e.g. by a prediction engine). A        probability of whether or not a potential chain accident will        occur is, by definition, not a parameter of the potential chain        accident.        x. predicting that an accident might occur—predicting a non-zero        probability that a potential accident will occur.        y. positive determining—in some embodiments, a determining is        made if a condition is true. One example of a condition is if        performing an action would achieve a given result. An example of        an action is changing a velocity of a motor-vehicle. An example        of a result is that a motor-vehicle accident will occur or that        a likelihood that a motor-vehicle accident will occur will be        increased.        If, in fact, it is determined that the condition is true, this        is a positive determining. For example, if it is determined that        performing the action would achieve the given result, this is an        example of a positive determining.        z. visible light and non-visible electromagnetic (EM)        radiation—the visible spectrum is the portion of the        electromagnetic spectrum that is visible to the human eye.        Electromagnetic radiation in this range of wavelengths is called        visible light or simply light. A typical human eye will respond        to wavelengths from about 390 to 700 nm.        Non-visible EM radiation is any EM radiation other than visible        light. In one example of non-visible EM radiation, a wavelength        of the EM radiation is greater than wavelengths of radiation in        the visible spectrum—e.g. a wavelength of the EM radiation        exceeds 800 nm or exceeds 900 nm or exceeds 1000 nm. One example        of EM radiation is infrared (IR) radiation. Another example is        radiofrequency (RF) radiation. Another example is microwave        radiation. It is noted that methods of transferring messages        between motor-vehicles using non-visible EM radiation are well        known in the art.        Preliminary Discussion—Examples where it is Useful to Control a        Motor-Vehicle According to Predictions of Potential Accidents        Involving Non-Adjacent Vehicles

Embodiments of the invention relate to computer-controlled driverlessmotor-vehicles that respond to computed predictions of potentialaccidents involving motor-vehicles that at least one of them is notdirectly adjacent to the computer-controlled motor-vehicle. In someembodiments, accident alerts comprising accident prediction data aretransmitted between motor-vehicles.

Without limitation, a number of examples where presently-disclosedteachings may be applied to reduce risk of involvement in accident(s)and/or to minimize severity of accident(s) are now presented. In allfour examples, a convoy of four vehicles drive in the same direction asillustrated in FIG. 1.

First Example—the Problem:

In the first example, it is desired to minimize a likelihood thatvehicle 100B will be involved in an accident. Vehicle 100B monitors itsdistance from both adjacent vehicles in the same lane—from vehicle 100Aand vehicle 100C, as these are the two vehicles that are adjacent tovehicle 100B and there is a risk of collision between either of thesevehicles and vehicle 100B. However, in reality vehicle 100D is alsoimportant for vehicle 100B—if vehicle 100D accelerates and strongly hitsvehicle 100C from behind, then vehicle 100C will “jump forward” becauseof the hit and might itself hit vehicle 100B. When vehicle 100C is hitfrom behind from vehicle 100D, this may cause vehicle 100C to travelforward at a speed faster than predicted by an onboard computer ofvehicle 100B. As a consequence, vehicle 100B might not have enough timeto react before vehicle 100C (i.e. which is travelling forward at speedthat is faster than expected) hits vehicle 100B from behind.

Second Example—the Problem—

In the second example, it is desired to minimize a likelihood thatvehicle 100C will be involved in an accident. Vehicle 100C monitors itsdistance from vehicle 100B and vehicle 100D, as these are the twovehicles that are adjacent to vehicle 100C and there is a risk ofcollision between either of these vehicles and vehicle 100C. However, inreality vehicle 100A is also important for vehicle 100C—if vehicle 100Asuddenly brakes and is hit by vehicle 100B, then vehicle 100B mightencounter an immediate stop because of the hit and might itself be hitby vehicle 100C. Because of hitting vehicle 100A, vehicle 100B'sstopping might be much faster than what vehicle 100C is expecting from anormally braking vehicle and vehicle 100C might not have enough time toreact before hitting vehicle 100B.

Other scenarios are also possible in which events related to even moredistant cars have effect on our car. For example, if in the above firstexample vehicle 100D is about to be hit from behind by another car(‘vehicle E’—NOT SHOWN—which is directly behind vehicle 100D) then a“chain accident” might occur that will eventually include vehicle E,vehicle 100D, vehicle 100C and vehicle 100B.

Comment on Transmitting Data—

It is noted that attempting to rely on receiving the accident alerts bynon-adjacent cars (thus solving at least some of the problematicaccident scenarios) may be problematic as this approach either requirespowerful and expensive transmitters because of the increased range weneed to cover, or it might result in a non-reliable system if thetransmission power is not high enough for good communication betweennon-adjacent cars.

According to some embodiments of the present invention, one vehiclealerts another vehicle about a potential accident, even though thatpotential accident involves a clash between the alerting car and a thirdvehicle.

First Example—Solution:

In the case of the first example above (when vehicle 100D is about tohit vehicle 100C), at some point in time vehicle 100C will detect it isabout to be hit by vehicle 100D or that there is a high probability(e.g. higher than some threshold probability) that it will be hit byvehicle 100D. Vehicle 100C will immediately transmit out that predictionas part of an accident alert, and this accident alert will be receivedby vehicle 100B. Note that at the time of receiving this accident alertat vehicle 100B, there might not yet be any indication from vehicle100B's own sensors that anything unusual is about to happen. Thus, theproposed solution increases the time available to vehicle 100B torespond.

Vehicle 100B may respond to the accident alert by whatever responsefound to be optimal under the circumstances. For example, vehicle 100Bmay immediately accelerate forward in order to minimize the danger ofbeing hit by vehicle 100C either as part of a chain accident or becausevehicle 100C is accelerating to avoid being hit by vehicle 100D.Additionally, vehicle 100B may also pass an alert to vehicle 100Acausing it to also accelerate so that vehicle 100B will not hit it frombehind while trying to avoid being hit by vehicle 100C. Alternatively,vehicle 100B may reach a conclusion (i.e. an onboard computer of vehicle100B may conclude) that the accident is unavoidable and then activateand hold its brakes as firmly as possible, so that when being hit itwill not move too much, thus lowering the risk to its passengers.

Other decision rules for selecting the optimal response to an accidentalert are also possible, and in following such rules vehicle 100B mayuse any information it has available. For example, the decision rule maydepend on the current speed of vehicle 100B, on the current speed ofvehicle 100C, on the current speed of vehicle 100D, on the current speedof vehicle 100A, on the distance between vehicle 100B and vehicle 100C,on the distance between vehicle 100C and vehicle 100D, on the distancebetween vehicle 100A and vehicle 100B, or on any combination of suchfactors. In order to enable a car to select an optimal response, alertmessages may include not only an accident alert but also informationabout speed of cars adjacent to the message sender and distances betweenthe message sender and the cars adjacent to it. Such information may becopied into secondary alerts triggered by an initial accident alert,thus spreading speed and distance information along the convoy todistant cars.

Second Example—Solution:

In the case of the second example above (when vehicle 100B is about tohit vehicle 100A), at some point in time vehicle 100B will detect it isabout to hit vehicle 100A or that there is a high probability (e.g.higher than some threshold probability) that it will hit vehicle 100A.In this example, vehicle 100B will immediately transmit out thatprediction, which will be received by vehicle 100C. Note that at thetime of receiving this accident alert at vehicle 100C there might notyet be any indication from vehicle 100C's own sensors that anythingunusual is about to happen. Thus, the proposed solution increases thetime available to vehicle 100C to respond.

Vehicle 100C may respond to the accident alert by whatever responsefound to be optimal under the circumstances. For example, vehicle 100Cmay immediately brake in order to minimize the danger of hitting vehicle100B. Additionally, vehicle 100C may also pass an alert to vehicle 100Dcausing it to immediately brake so that vehicle 100D will not hitvehicle 100C from behind while vehicle 100C is trying to avoid hittingvehicle 100B. Alternatively, vehicle 100C may reach a conclusion (i.e.an onboard computer of vehicle 100C may conclude) that the accident isunavoidable and then adjust its speed to some optimal value that isconsidered to be optimal in the sense that it minimizes the overalldamage to vehicle 100C passengers when vehicle 100C is being caught inthe middle between vehicle 100B and vehicle 100D in a chain accident.

Here too, other decision rules for selecting the optimal response to anaccident alert are also possible, and in following such rules vehicle100C may use any information it has available. For example, the decisionrule may depend on the current speed of vehicle 100C, on the currentspeed of vehicle 100B, on the current speed of vehicle 100A, on thecurrent speed of vehicle 100D, on the distance between vehicle 100B andvehicle 100C, on the distance between vehicle 100A and vehicle 100B, onthe distance between vehicle 100C and vehicle 100D, or on anycombination of such factors.

While the above discussion emphasized the use of accident alertsreceived in a given car in responding to accidents about to occurbetween two other cars, the benefits of the proposed idea are notlimited to such case. For example, if vehicle 100C determines (i.e. anonboard computer of vehicle 100C determines) that it is about to hitvehicle 100B or that there is a high probability that it will hitvehicle 100B, then vehicle 100C will immediately transmit out thatprediction (for the benefit of vehicle 100A and for the benefit ofvehicle 100D and the car behind vehicle 100D). But vehicle 100B can alsoreceive that information and benefit from it. Even though vehicle 100Bis expected to learn about being hit from behind by vehicle 100C usingits own sensors, it may be the case that its sensors are slow to respondby some reason or even that its sensors had failed and cannot detect theforthcoming accident. In other words, the accident alert by vehicle 100Cacts as a backup for vehicle 100B's sensors and may either increasevehicle 100B's response time or may even be the only source for alertingvehicle 100B.

General Comments—

It should be noted that while the explanations and examples in thisdisclosure are presented in the context of cars driving in a convoy, theinvention is also useful in the context of cars driving in otherconfigurations. It should also be noted that while the explanations andexamples in this disclosure are presented in the context of driverlesscars, the invention is also useful in the context of regularhuman-driven cars.

A Discussion of FIGS. 4-6

FIG. 4 illustrates a method for attempting to avoid a potentialmotor-vehicle accident according to some embodiments. FIG. 5 illustratesan exemplary anti-accident device 200 which may be disposed into anymotor-vehicle that participates in the method of FIG. 4. Theanti-accident device 200 of FIG. 5 temporarily or permanently resideswithin a host motor-vehicle and thus is an onboard device of themotor-vehicle. In the illustrated example of FIG. 5, the anti-accidentdevice 200 includes (i) a prediction-engine 220 for processing factualinput data about a plurality of motor-vehicles and computationallypredicting an accident scenario, thereby generating output predictiondata of a potential accident; (ii) a wireless transmitter 230 forwirelessly transmitting non-visual EM signals; (iii) a wireless receiver240 for wirelessly receiving non-visual EM signals; and (iv) a devicecontroller 210 for sending control signals to onboard vehicle controlsof the host motor-vehicle where the anti-accident device resides.

In some embodiments, all components of the anti-accident device 200 arein wired communication with each other and/or in wired communicationwith at least one of the onboard vehicle controls of the hostmotor-vehicle.

Anti-accident device 200 may include a digital computer (not illustratedin FIG. 5). For example, either or both of device controller 210 andprediction engine 220 may be implemented as a digital computer executingsoftware. In one example, device controller 210 and prediction engine220 may be implemented by separate digital computers. In anotherexample, a common digital computer executes software to provide thefunctionality of both device controller 210 and prediction engine 220.

Any element illustrated in FIG. 5 may include and/or be implemented in“electronic circuitry,” defined above. Furthermore, the skilled artisanwill appreciate that although wireless transmitter 230 and receiver 240are illustrated as separate units, they may be implemented as a singletransceiver unit.

The method of FIG. 4 requires three motor-vehicles, For example, arespective anti-accident device 200 respectively resides in each of thethree motor-vehicles and respectively controls its host vehicle.

FIGS. 6A-6E and 8A-8E respectively illustrate two non-limiting use casesof the method of FIG. 4. Although these use cases are non-limiting, themethod of FIG. 4 will first be explained with reference to FIGS. 6A-6E.The use case of FIGS. 6A-6E illustrates a convoy of motor-vehiclestravelling in the same direction where initially (FIG. 6A) (i)motor-vehicles 100B and 100C are travelling at the same speed and (ii)front motor-vehicle 100A is travelling at a lower speed.

In the non-limiting example of FIG. 6A, a second motor-vehicle 100Cfollows a first motor-vehicle 100B, and a third motor-vehicle 100Dfollows the second motor-vehicle 100C. The first motor-vehicle 100Bfollows a fourth motor-vehicle 100A.

In step S101 of FIG. 4, a first accident alert is wirelessly transmittedby non-visual EM radiation from a first motor-vehicle (e.g. vehicle 100Bof FIG. 6A)—for example, by an onboard wireless transmitter 230 of anonboard anti-accident device 200 that resides in vehicle 100B. The firstaccident alert comprises accident prediction data about a potentialmotor-vehicle accident—for example, a potential accident where vehicle100B hits vehicles 100A from behind. For example, the accidentprediction data is generated by an onboard computer (i.e. by an onboardprediction engine 220 implemented at least in part by a digitalcomputer) of the first vehicle 100B according to factual input data—e.g.input data about the relative speeds of the first 100B vehicle andfourth vehicle 100A.

In this example, at least some of the factual input data employed forgenerating this accident prediction data may be unavailable to a secondmotor-vehicle 100C. For example, the second motor-vehicle 100C mayinclude front-looking sensors that monitor a speed of a vehicle 100Bimmediately in front of the second motor-vehicle 100C—thesefront-looking sensors may not be able to monitor a speed of the fourthvehicle 100A. For example, a presence of first vehicle 100B may block anoptical path between the second vehicle 100C and the fourth vehicle100A.

In some embodiments, some or all of the accident prediction data of thefirst accident alert may be computed by an onboard computer and/orprediction engine of an anti-accident device residing in the firstvehicle.

In step S121 (e.g. see FIG. 6B) the first accident alert is received atthe second motor-vehicle (e.g. vehicle 100C)—for example, by an onboardwireless receiver 240 of an onboard anti-accident device 200 thatresides in the second vehicle (e.g. vehicle 100C). Optionally, anonboard computer (e.g. of a respective anti-accident device 200) on thesecond motor-vehicle (e.g. vehicle 100C) analyzes the content of thefirst accident alert—a discussion about content of the accident alertsand analysis of content thereof is provided below.

Step S141 is performed in response to the receiving of the firstaccident alert at the second motor-vehicle (e.g. vehicle 100C). In stepS141, a second accident alert is wirelessly transmitted by non-visual EMradiation and from the second motor-vehicle—for the non-limiting usecase of FIGS. 6A-6E, step S141 is illustrated in FIG. 6C. As will bediscussed below, the content of the first and second accident alerts maybe the same, in which case the second vehicle 100C relays only thecontent received in the first accident alert. In another example, thecontent of the first and second accident alerts may be different—e.g. anonboard computer of the second vehicle may, for example, update anoutcome prediction related to the first accident alert. For example,onboard instruments of the second vehicle may acquire additional factualinput data which is used to refine accident prediction data associatedwith the first accident alert, and this refined prediction data may beincluded in the second accident alert.

In step S161 (e.g. see FIG. 6D) the second accident alert is received atthe third motor-vehicle (e.g. vehicle 100D)—for example, by an onboardwireless receiver 240 of an onboard anti-accident device 200 thatresides in the third motor-vehicle (e.g. vehicle 100D).

Step S181 is performed in response to the receiving of the secondaccident alert at the third motor-vehicle (e.g. vehicle 100D). In stepS181, an onboard computer (e.g. prediction-engine 220 that isimplemented by a digital computer executing software) of the thirdmotor-vehicle (e.g. vehicle 100D) performs at least one vehicle controlaction—for example, by sending control signals to onboard vehiclecontrols of the host motor-vehicle where the anti-accident device 200resides. The vehicle control action(s) are performed so as to attempt(i) to avoid being involved in the potential motor-vehicle accidentand/or (ii) to minimize damage inflicted upon the third motor-vehicle asa result of involvement in the potential motor-vehicle accident byperforming at least one vehicle control action.

Thus, FIG. 6E relates to one implementation of step S181 for theparticular use-case of FIGS. 6A-6E. In this example, to avoid beinginvolved in the potential accident between first 100B and second 100Cvehicles, the third vehicle 100D may brake and/or decelerate—this isillustrated in FIG. 6E where the velocity arrow on vehicle 100D is oflesser magnitude than the velocity arrow on vehicle 100D in FIG. 6D.

In the above example, the trigger of the seconding of the first accidentalert was the fourth vehicle 600A—i.e. the potential of an accidentbetween the first 600B and fourth 600A vehicles. In this sense, thesecond vehicle 600C may take advantage of the sensors of vehicle 600Bwhich accesses input factual data that may not be available to thesecond vehicle 600C (e.g. due to a presence of the first 600B vehicleblocking a line-of-sight from the second vehicle 600C to the fourthvehicle 600A).

In another example, an action performed by the first 600B vehicle itselfmay trigger the sending of the first accident alert. For example, thefirst vehicle 600B may drive over an unexpected patch of bad road whichcauses the first vehicle 600B to decelerate. First vehicle 600B thensends an accident alert warning second vehicle 600C of a potentialaccident that might occur if second vehicle 600C does not slow down. Inthis example, the deceleration of first vehicle 600B may eventually bedetectable by sensors of the second vehicle 600C, but there is anadvantage in alerting second vehicle 600C by first vehicle 600B becausefirst vehicle 600B may be aware of the potential accident earlier thanthe sensors of second vehicle 600C.

The method of FIG. 4 may be performed at any speed—in some embodiments,an elapsed time between commencement of step S101 and performance ofstep S181 is at most 500 milliseconds or at most 300 milliseconds or atmost 100 milliseconds.

Anti-Accident Devices and Some Embodiments of the Method of FIG. 4

In some embodiments of the invention, a respective anti-accident device200 resides (i.e. temporarily or permanently) on every motor vehicle ofthe three motor-vehicles referred to in the method of FIG. 4. Eachanti-accident device 200 is capable of providing all the functionalityrequired from the first, second and third motor vehicles of the methodof FIG. 4—the particular functionality depends on the vehicle where theanti-accident device resides.

Thus, when the anti-accident device 200 resides on the first vehicle,the anti-accident device provides the following functionality: inresponse to a predicting, by the prediction engine (i.e. of theanti-accident device 200 on the first vehicle) of an accident scenarioabout a first potential motor-vehicle accident (the ‘first potentialmotor-vehicle accident’ corresponds to the ‘potential motor vehicleaccident’ of steps S101 and S181), the device controller (i.e. of theanti-accident device 200 on the first vehicle) transmits (see step S101of FIG. 4), via the wireless transmitter (i.e. of the anti-accidentdevice 200 on the first vehicle), a first outgoing accident alertcomprising accident prediction data about the first potentialmotor-vehicle accident.

When the anti-accident device 200 resides on the second vehicle of FIG.4, the anti-accident device provides the following functionality: inresponse to a receiving (see step S121), via the wireless receiver (i.e.of the anti-accident device 200 on the second vehicle), of a firstincoming accident alert (i.e. the ‘first incoming accident alert’corresponds to the ‘first accident alert’ of steps S121 and S141 of FIG.4) comprising accident prediction data about a second potentialmotor-vehicle accident (the ‘second potential motor-vehicle accident’corresponds to the ‘potential motor vehicle accident’ of steps S101 andS181), the device controller (i.e. of the anti-accident device 200 onthe second vehicle) transmits, via the wireless transmitter (i.e. of theanti-accident device 200 on the second vehicle), a second outgoingaccident alert (i.e. the ‘second outgoing accident alert’ corresponds tothe ‘second accident alert’ of steps S141 and S161 of FIG. 4) comprisingaccident prediction data for the second potential motor-vehicleaccident.

When the anti-accident device 200 resides on the third vehicle of FIG.4, the anti-accident device provides the following functionality: inresponse to a receiving, via the wireless receiver (i.e. of theanti-accident device 200 on the third vehicle), of a second incomingaccident alert (i.e. the ‘second incoming accident alert’ corresponds tothe ‘second accident alert’ of steps S141 and S161 of FIG. 4) comprisingaccident prediction data about a third potential motor-vehicle accident(the ‘third potential motor-vehicle accident’ corresponds to the‘potential motor vehicle accident’ of steps S101 and S181) between twoor more external motor-vehicles (i.e. in this case, each of the externalmotor vehicles is a vehicle other than the ‘third’ motor vehicle of FIG.4—in FIGS. 6A-6E vehicles 600A-600C are the external vehicles; in FIGS.8A-8E vehicles 120B-120D are the external vehicles), the devicecontroller (i.e. of the anti-accident device 200 on the third vehicle)sends control signals to one or more onboard vehicle controls of thehost motor-vehicle (i.e. the host motor vehicle corresponds to the thirdmotor vehicle of FIG. 4) so as (A) to avoid involvement, of the hostmotor-vehicle (i.e. which corresponds to the third motor vehicle of FIG.4—e.g. vehicle 600D of FIGS. 6A-6E or vehicle 120A of FIGS. 8A-8E), inthe third potential motor-vehicle accident; and/or (B) to reduce (e.g.minimize) damage inflicted upon the host motor-vehicle (i.e. whichcorresponds to the third motor vehicle of FIG. 4) as a result ofinvolvement in the third potential motor-vehicle accident by performingat least one vehicle control action.

A Discussion of FIGS. 7A-7B

FIG. 7A is similar to FIG. 4 but (i) includes an extra step S123 inwhich in response to the receiving of the first accident alert,computing an updated outcome prediction by an onboard computer of thesecond motor-vehicle, thereby generating updated accident predictiondata and (ii) replaces step S141 of FIG. 4 with step S143 of FIG. 7A.

In some embodiments, (i) one or more onboard computer(s) of the firstmotor-vehicle (vehicle 600B of FIGS. 6A-6E) may compute accidentprediction data of the first accident alert from a first set of factualinput data; and (ii) one or more onboard computer(s) of the secondmotor-vehicle computes accident prediction data of the second accidentalert from a second set of factual input data that includes factualinput data not present within the first set of factual input data. Inone example, when the onboard computer(s) of the first motor-vehiclecomputes a probability of collision between the first (vehicle 600B ofFIGS. 6A-6E) and second (vehicle 600C of FIGS. 6A-6E) motor-vehicles,the onboard computer(s) of the first motor-vehicle may not haveavailable information (or may have inaccurate information) about thebraking capability of the second vehicle 600C. As such, the computedprobability of collision might not necessarily be accurate. In thisfirst example, accurate information about the braking capability of thesecond vehicle 600C is available to the onboard computer of the secondmotor-vehicle (vehicle 600C of FIGS. 6A-6E) and this accurateinformation about braking capabilities of the second vehicle (vehicle600C of FIGS. 6A-6E) may serve as factual input data in step S123.

In a second example, a device (e.g. employing sensing technologydisclosed in US 20140297111 or US 20140365142) is (i) installed in thesecond vehicle 600C and (ii) is in wired communication, within thesecond vehicle 600C, with an onboard computer of the second motorvehicle 600C. When the onboard computer(s) of the first motor-vehiclecomputes a probability of collision between the first (vehicle 600B ofFIGS. 6A-6E) and second (vehicle 600C of FIGS. 6A-6E) motor-vehicles,the onboard computer(s) of the first motor-vehicle may not haveavailable information (or may have inaccurate information) about theblood alcohol level of the driver of the second vehicle 600C (e.g. thismay be descriptive of a reaction-time of the driver of the secondvehicle 600C). As such, the computed probability of collision might notnecessarily be accurate. In this second example, accurate informationabout the blood alcohol level of the driver of the second vehicle 600Cis available to the onboard computer of the second motor-vehicle(vehicle 600C of FIGS. 6A-6E) and this accurate information about bloodalcohol level of the driver of the second vehicle (vehicle 600C of FIGS.6A-6E) may serve as factual input data in step S123.

In step S143, the second accident alert transmitted from the secondmotor-vehicle (vehicle 600C of FIGS. 6A-6E) comprises the updatedaccident prediction data based upon the accurate braking capability dataof the second vehicle 600C or the blood alcohol level of the driver ofthe second vehicle 600C.

FIG. 7B is similar to FIG. 4 but includes extra steps S125, S129 andS131. FIG. 7B relates to some examples where the accident predictiondata of the received first accident alert is evaluated at the secondmotor-vehicle and the transmitting of the second accident alert from thesecond motor-vehicle is contingent upon the results of the evaluation.

In step S125, onboard computer of the second motor-vehicle evaluatesS125 accident prediction data of the first accident alert—for example,to determine if a risk of an accident exceeds a risk-threshold or if aseverity of an accident exceeds a severity-threshold. It may decide torefrain from performing step S141 for low-risk situations—for example,to avoid burdening the onboard computer of the third motor-vehicle or toavoid situations where the third motor-vehicle would needlessly changeits velocity.

Thus, in step S129, it is determined (e.g. by onboard computer of thesecond motor-vehicle) if the results of the evaluating justifytransmitting the second accident alert. If not (step S131) the secondaccident alert is not transmitted.

FIGS. 7A and 7B illustrate different potential modifications of themethod of FIG. 4—the skilled artisan will appreciate that thesemodifications may be combined in a single method.

A Discussion of FIGS. 8A-8E

FIG. 4 was explained above for the particular example of FIGS. 6A-6Ewhere the second motor 100B vehicle follows the first motor-vehicle 100Aand the third motor-vehicle 100C follows the second motor-vehicle. Thisis not a limitation.

In the example of FIGS. 8A-8E, the second motor-vehicle 120B follows thethird motor-vehicle 120A and the first motor-vehicle 120C follows thesecond motor-vehicle 120B. A fourth motor-vehicle 120D follows the firstmotor vehicle 120C.

In step S101 of FIG. 4, a first accident alert is wirelessly transmittedby non-visual EM radiation from a first motor-vehicle (e.g. vehicle 120Cof FIG. 8A)—for example, by an onboard wireless transmitter 230 of anonboard anti-accident device 200 that resides in vehicle 120C. The firstaccident alert comprises accident prediction data about a potentialmotor-vehicle accident—for example, a potential accident where vehicle120D hits vehicle 120C from behind and thus vehicles in front of vehicle120C are at risk of being involved in a chain accident.

In step S121 (e.g. see FIG. 8B) the first accident alert is received atthe second motor-vehicle (e.g. vehicle 120B)—for example, by an onboardwireless receiver 240 of an onboard anti-accident device 200 thatresides in the second vehicle (e.g. vehicle 120B).

Step S141 in performed in response to the receiving of the firstaccident alert at the second motor-vehicle (e.g. vehicle 120B). In stepS141, a second accident alert is wirelessly transmitted by non-visual EMradiation and from the second motor-vehicle 120B—for the non-limitinguse case of FIGS. 8A-8E, step S141 is illustrated in FIG. 8C. Forexample, this second accident alert may indicate that there is anon-zero probability that the second car 120B will be hit from behind.

In step S161 (e.g. see FIG. 8D) the second accident alert is received atthe third motor-vehicle (e.g. vehicle 120A)—for example, by an onboardwireless receiver 240 of an onboard anti-accident device 200 thatresides in third motor-vehicle (e.g. vehicle 120A).

Step S181 is performed in response to the receiving of the secondaccident alert at the third motor-vehicle (e.g. vehicle 120A). In stepS181, an onboard computer (e.g. prediction-engine 220 that isimplemented by a digital computer executing software) of the thirdmotor-vehicle (e.g. vehicle 120A) performs at least one vehicle controlaction—for example, turning to the right onto the shoulder of the roadto avoid being involved in the chain accident that would be triggered byvehicle 120C hitting vehicle 120B from behind.

In FIG. 8E, vehicle 120A according to step S181 is moving forward and tothe right towards the shoulder of the road.

A Discussion of FIGS. 9, 10A-10E

FIG. 9 is a flow chart of a method for responding to a prediction of apotential car accident involving first, second and third motor-vehicles.Without limitation, the method of FIG. 9 will be explained withreference to the non-limiting example of FIGS. 10A-10E—thus, in thenon-limiting example, the first motor-vehicle is vehicle 100B, thesecond vehicle is vehicle 100C, and the third vehicle is vehicle 100A.

Thus, in this example, (i) the second motor 100C vehicle is behind thefirst 100B motor-vehicle and (ii) the first 100B motor-vehicle is behindthe third 100A motor-vehicle.

In step S201 (e.g. see FIG. 10A), an onboard computer of a firstmotor-vehicle 100B computationally predicts an accident scenarioindicating that the first 100B motor-vehicle might be hit from behind bya second motor-vehicle 100C.

In step S205 (e.g. see FIG. 10B), in response to the predicting, anaccident alert is wirelessly transmitted, by non-visual EM radiation andfrom the first motor-vehicle 100B.

In step S209 (e.g. see FIG. 10C), the accident alert is received by athird motor-vehicle 100A that is in front of the first 100Bmotor-vehicle.

In step S213 (e.g. see FIG. 10D), in response to the receiving of theaccident alert, an onboard computer of the third motor vehicle 100Aattempts by performing at least one vehicle control action: (i) to avoidbeing hit from behind by the first motor-vehicle 100B and/or (ii) toreduce damage inflicted upon the third motor-vehicle 100A resulting frombeing hit from behind by the first motor-vehicle 100B.

The result is illustrated in FIG. 10E where the velocity of travel ofvehicle 100A changes. In particular, vehicle 100A moves into the leftlane to avoid being hit from behind by vehicle 100B.

The method of FIG. 9 may be performed at any speed—in some embodiments,an elapsed time between commencement of step S201 and performance ofstep S213 is at most 500 milliseconds or at most 300 milliseconds or atmost 100 milliseconds.

Anti-Accident Devices and Some Embodiments of the Method of FIG. 9

In some embodiments of the invention, a respective anti-accident device200 resides (i.e. temporarily or permanently) on the first (e.g. 100B ofFIGS. 10A-10E) and third (e.g. 100A of FIGS. 10A-10E) motor vehicles ofthe method of FIG. 9.

Each anti-accident device 200 is capable of providing all thefunctionality required from the first and third motor vehicles of themethod of FIG. 9—the particular functionality depends on the vehiclewhere the anti-accident device resides.

Thus, when the anti-accident device 200 resides on the first vehicle(e.g. 100B of FIGS. 10A-10E) of the method of FIG. 9, the anti-accidentdevice provides the following functionality: in response to a predicting(e.g. performed in step S201 of FIG. 9) by the prediction-engine of theanti-accident device residing on the first vehicle (i.e. which is thehost motor-vehicle device of the first anti-accident device—e.g. 100B ofFIGS. 10A-10E) that the host motor-vehicle (i.e. first vehicle—e.g. 100Bof FIGS. 10A-10E) might be hit from behind by a first externalmotor-vehicle (i.e. the first external motor-vehicle is equivalent tothe second motor vehicle of FIG. 9—e.g. 100C of FIGS. 10A-10E), thefirst anti-accident device (i.e. residing on the first vehicle—e.g. 100Bof FIGS. 10A-10E) transmits (e.g. see step S205 of FIG. 9) an outgoingaccident alert (e.g. via the wireless transmitter of the anti-accidentdevice residing on the first vehicle).

When the anti-accident device 200 resides on the third vehicle (e.g.100A of FIGS. 10A-10E) of the method of FIG. 9, the anti-accident deviceprovides the following functionality: in response to a receipt of anincoming accident alert (see step S209 of FIG. 9) that: (A) is receivedvia a wireless receiver of the anti-accident device on the third vehicle(e.g. 100A of FIGS. 10A-10E); (B) is received from a second externalmotor-vehicle (i.e. the second external motor-vehicle is equivalent tothe first motor vehicle of FIG. 9—e.g. 100B of FIGS. 10A-10E) that isbehind of the host motor-vehicle (i.e. when the anti-accident deviceresides on the third vehicle, the host motor-vehicle is the thirdvehicle—e.g. 100A of FIGS. 10A-10E); and (C). indicates that an accidentmight occur behind the host motor-vehicle (e.g. 100A of FIGS. 10A-10E)where the second external motor-vehicle (e.g. 100B of FIGS. 10A-10E) ishit from behind by a third external motor-vehicle (e.g. 100C of FIGS.10A-10E), the device controller of the anti-accident device residing onthe third vehicle of FIG. 9 (e.g. 100A of FIGS. 10A-10E) sends controlsignals to one or more onboard vehicle controls of the hostmotor-vehicle (e.g. 100A of FIGS. 10A-10E). In particular, the controlsignals are sent so as to perform at least one vehicle control action inorder to avoid the host motor-vehicle (i.e. this is the third vehicle ofFIG. 9—e.g. 100A of FIGS. 10A-10E) being hit from behind by the secondexternal motor-vehicle (i.e. this is the first vehicle of FIG. 9—e.g.100B of FIGS. 10A-10E) and/or in order to reduce damage inflicted uponthe host motor-vehicle (e.g. 100A of FIGS. 10A-10E) resulting from beinghit from behind by the second external motor-vehicle (i.e. this is thefirst vehicle of FIG. 9—e.g. 100B of FIGS. 10A-10E).

A Discussion of FIGS. 11-12

FIG. 11 is a flow chart of a method for responding to a prediction of apotential accident involving first 100B, second 100A and third 100Cmotor-vehicles according to some embodiments of the invention. Withoutlimitation, the method of FIG. 11 will be explained with reference tothe non-limiting example of FIG. 12—thus, in the non-limiting example,the first motor-vehicle is vehicle 100B, the second motor-vehicle isvehicle 100A, and the third motor-vehicle is vehicle 100C.

In step S301, an accident scenario is computationally predicted by anonboard computer of the first motor-vehicle 100B, the accident scenarioindicating that a first motor-vehicle accident might occur between thefirst 100B and second 100A motor-vehicles—e.g. where the first 100Bmotor-vehicle hits the second 100A motor-vehicle from behind. Forexample, as shown in FIG. 12, first vehicle 100B is travelling fasterthan second vehicle 100A.

In step S305 an onboard computer of the first 100B motor-vehicledetermines if changing a velocity of the first 100B motor-vehicle (e.g.by braking sharply) in order to (i) avoid the first motor-vehicleaccident (i.e. where the first 100B motor-vehicle hits the second 100Amotor-vehicle from behind) and/or (ii) reduce a likelihood thereofand/or (iii) reduce a severity thereof would (i) result in a secondmotor-vehicle accident between the first 100B and third 100Cmotor-vehicles (e.g. the third 100C motor-vehicle hits the first 100Bmotor-vehicle from behind) and/or (ii) increases a likelihood of thesecond motor-vehicle accident. In some embodiments, step S305 isperformed in response to the predicting of step S301.

For example, as shown in FIG. 12, the third motor-vehicle 100C istravelling faster than the first 100B motor-vehicle. In the event thatthe first 100B motor-vehicle brakes sharply, this could cause the third100C motor-vehicle to hit the first 100B motor-vehicle from behind.

In step S309, in response to a positive determining (i.e. a determiningthat in fact the changing of the velocity of the first motor-vehicle100B to avoid the first motor-vehicle accident would cause the secondmotor-vehicle accident to occur or could increase a likelihood thereof),an onboard computer of the first motor-vehicle 100B performs at leastone vehicle control action by adjusting the velocity of the firstmotor-vehicle 100B according to respective velocities and/oraccelerations of the second 100A and third motor 100C vehicles.

The method of FIG. 11 may be performed at any speed—in some embodiments,an elapsed time between commencement of step S301 and performance ofstep S309 is at most 500 milliseconds or at most 300 milliseconds or atmost 100 milliseconds.

Anti-Accident Devices and Some Embodiments of the Method of FIG. 11

In some embodiments of the invention, anti-accident device 200 resides(i.e. temporarily or permanently) on the first motor vehicle of themethod of FIG. 11 (e.g. 100B of FIG. 12).

This anti-accident device comprises a prediction-engine 220 forprocessing factual input data about a plurality of motor-vehicles andcomputationally predicting an accident scenario indicating that a firstmotor vehicle accident may occur between the host motor-vehicle (i.e.the first vehicle of FIG. 11—e.g. 100B of FIG. 12) and a first externalmotor-vehicle (i.e. to the second motor-vehicle of FIG. 11—e.g. 100C ofFIG. 12).

The prediction-engine is further operative to determine if changing avelocity of the host motor-vehicle (i.e. the first vehicle of FIG.11—e.g. 100B of FIG. 12) in order (i) to avoid the first motor-vehicleaccident and/or (ii) to reduce a likelihood thereof and/or (iii) toreduce a severity thereof, would result in one or more of: (A) a secondmotor-vehicle accident occurring between the host motor-vehicle (i.e.the first vehicle of FIG. 11—e.g. 100B of FIG. 12) and a second externalmotor-vehicle (i.e. the third vehicle of FIG. 11—e.g. 100A of FIG. 12)and (ii) an increase in a likelihood that the second motor-vehicleaccident will occur.

This anti-accident device further comprises a device controller 210 forresponding to a positive determining by sending control signals (e.g.wired control signals) to one or more onboard vehicle controls of thehost motor-vehicle (i.e. the first vehicle of FIG. 11—e.g. 100B of FIG.12) to adjust the velocity of the host motor-vehicle (i.e. the firstvehicle of FIG. 11—e.g. 100B of FIG. 12) according to respectivevelocities and/or accelerations of the first external motor-vehicle(i.e. the second vehicle of FIG. 11—e.g. 100C of FIG. 12) and the secondexternal motor-vehicle (i.e. the third vehicle of FIG. 11—e.g. 100A ofFIG. 12).

Additional Discussion

In the current section, vehicle 100A is referred to as car A, vehicle100B is referred to as car B, vehicle 100C is referred to as car C,vehicle 100D is referred to as car D. In the present example, it isassumed that car B follows car A, car C follows car B and car D followscar C, as illustrated in FIG. 1

A first method is disclosed that is most useful in managing driverlesscars driving in a convoy, but may also be used for cars driving inconfigurations other than a convoy and for human-driven cars. The methodis about sending an accident alert by one car to another car in responseto being alerted by yet another car. The method comprises the followingsteps:

-   -   a. transmitting, by a first car, a first accident alert;    -   b. receiving the first accident alert by a second car;    -   c. In response to the receiving of the first accident alert,        transmitting a second accident alert by the second car;    -   d. receiving the second accident alert by a third car;    -   e. In response to the receiving of the second accident alert,        attempting to avoid a car accident by the third car.

The first accident alert may comprise an indication that the first caris braking. This corresponds for example to a case where the first caris car B, the second car is car C, car B brakes and the first accidentalert is sent by car B and received by car C.

The first accident alert may comprise an indication that the first caris decelerating. This corresponds for example to a case where the firstcar is car B, the second car is car C, car B decelerates and the firstaccident alert is sent by car B and received by car C.

The first accident alert may comprise an indication that the first caris accelerating. This corresponds for example to a case where the firstcar is car C, the second car is car B, car C accelerates and the firstaccident alert is sent by car C and received by car B.

The first accident alert may comprise an indication of an action by afourth car. This corresponds for example to a case where the first caris car C, the second car is car B, the fourth car is car D, car Daccelerates and the first accident alert is sent by car C and receivedby car B.

The first accident alert may comprise an indication that a car accidentmight occur between the first car and the second car. By an “alertcomprising an indication that a car accident might occur” it is meant(here and in all other places this term is used in this disclosure) thatan alert includes an explicit indication of the fact that an accidentmight occur, with or without an identification of a root cause for theaccident (such as braking, decelerating or accelerating by a car). Thiscorresponds for example to a case where the first car is car C, thesecond car is car B, car C accelerates and the first accident alert issent by car C and received by car B.

The first accident alert may comprise an indication that a car accidentmight occur between the first car and a fourth car. This corresponds forexample to a case where the first car is car C, the second car is car B,the fourth car is car D, car D accelerates and the first accident alertis sent by car C and received by car B.

The second accident alert may comprise an indication that the first caris braking. This corresponds for example to a case where the first caris car B, the second car is car C, the third car is car D, car B brakes,the first accident alert is sent by car B and received by car C and thesecond accident alert is sent by car C and received by car D.

The second accident alert may comprise an indication that the first caris decelerating. This corresponds for example to a case where the firstcar is car B, the second car is car C, the third car is car D, car Bdecelerates, the first accident alert is sent by car B and received bycar C and the second accident alert is sent by car C and received by carD.

The second accident alert may comprise an indication that the first caris accelerating. This corresponds for example to a case where the firstcar is car C, the second car is car B, the third car is car A, car Caccelerates, the first accident alert is sent by car C and received bycar B and the second accident alert is sent by car B and received by carA.

The second accident alert may comprise an indication of an action by afourth car. This corresponds for example to a case where the first caris car C, the second car is car B, the third car is car A, the fourthcar is car D, car D accelerates, the first accident alert is sent by carC and received by car B and the second accident alert is sent by car Band received by car A.

The second accident alert may comprise an indication that a car accidentmight occur between the first car and the second car. This correspondsfor example to a case where the first car is car C, the second car iscar B, the third car is car A, car C accelerates, the first accidentalert is sent by car C and received by car B and the second accidentalert is sent by car B and received by car A.

The second accident alert may comprise an indication that a car accidentmight occur between the first car and a fourth car. This corresponds forexample to a case where the first car is car C, the second car is car B,the third car is car A, the fourth car is car D, car D accelerates, thefirst accident alert is sent by car C and received by car B and thesecond accident alert is sent by car B and received by car A.

The second car may follow the first car and the third car may follow thesecond car. This corresponds for example to a case where the first caris car B, the second car is car C, the third car is car D, car B brakes,the first accident alert is sent by car B and received by car C and thesecond accident alert is sent by car C and received by car D.

Alternatively, the second car may follow the third car and the first carmay follow the second car. This corresponds for example to a case wherethe first car is car C, the second car is car B, the third car is car A,car C accelerates, the first accident alert is sent by car C andreceived by car B and the second accident alert is sent by car B andreceived by car A.

The attempting to avoid a car accident may comprise braking by the thirdcar. This corresponds for example to a case where the first car is carB, the second car is car C, the third car is car D, car B brakes, thefirst accident alert is sent by car B and received by car C, the secondaccident alert is sent by car C and received by car D and car D brakesin an attempt to avoid hitting car C.

The attempting to avoid a car accident may comprise decelerating by thethird car. This corresponds for example to a case where the first car iscar B, the second car is car C, the third car is car D, car B brakes,the first accident alert is sent by car B and received by car C, thesecond accident alert is sent by car C and received by car D and car Ddecelerates in an attempt to avoid hitting car C.

The attempting to avoid a car accident may comprise accelerating by thethird car. This corresponds for example to a case where the first car iscar C, the second car is car B, the third car is car A, car Caccelerates, the first accident alert is sent by car C and received bycar B, the second accident alert is sent by car B and received by car Aand car A accelerates in an attempt to avoid being hit by car B.

A second method is disclosed that is most useful in managing driverlesscars driving in a convoy, but may also be used for cars driving inconfigurations other than a convoy and for human-driven cars. The methodis about alerting a car in front of us that we are about to be hit frombehind. The method comprises the following steps:

-   -   a. determining, by a first car, that a car accident might occur        between the first car and a second car with the second car        hitting the first car from behind;    -   b. transmitting, by the first car, an accident alert;    -   c. receiving the accident alert by a third car which is in front        of the first car;    -   d. in response to the receiving of the accident alert,        attempting to avoid a car accident by the third car.

This method corresponds for example to a case where the first car is carB, the second car is car C, the third car is car A, car C accelerates,the accident alert is sent by car B and received by car A and car Aattempts to avoid being hit by car B.

The accident alert may comprise an indication that the first car mightbe hit by the second car from behind.

The accident alert may comprise an indication that a car accident mightoccur between the first car and the third car.

The attempting to avoid a car accident may comprise accelerating by thethird car.

A third method is disclosed that is most useful in managing driverlesscars driving in a convoy, but may also be used for cars driving inconfigurations other than a convoy and for human-driven cars. The methodis about adjusting car speed for minimizing damage when being hit frombehind and hitting another car in the front. The method comprises thefollowing steps:

-   -   a. determining, by a first car, that a first car accident might        occur between the first car and a second car;    -   b. determining, by the first car, that changing its speed in        order to avoid the first car accident with the second car would        result in the first car having a second car accident with a        third car;    -   c. in response to the determining, adjusting the speed of the        first car according to the speed of the second car and according        to the speed of the third car.

The adjusted speed of the first car may be selected so as to reduce theamount of an overall damage suffered by the first car from the first caraccident and the second car accident.

The first car may follow the second car and the third car may follow thefirst car. This corresponds for example to a case where the first car iscar C, the second car is car B, the third car is car D, car Bdecelerates and car C adjusts its speed.

Alternatively, the first car may follow the third car and the second carmay follow the first car. This corresponds for example to a case wherethe first car is car C, the second car is car D, the third car is car B,car D accelerates and car C adjusts its speed.

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All references cited herein are incorporated by reference in theirentirety. Citation of a reference does not constitute an admission thatthe reference is prior art.

It is further noted that any of the embodiments described above mayfurther include receiving, sending or storing instructions and/or datathat implement the operations described above in conjunction with thefigures upon a computer readable medium. Generally speaking, a computerreadable medium (e.g. non-transitory medium) may include storage mediaor memory media such as magnetic or flash or optical media, e.g. disk orCD-ROM, volatile or non-volatile media such as RAM, ROM, etc.

Having thus described the foregoing exemplary embodiments it will beapparent to those skilled in the art that various equivalents,alterations, modifications, and improvements thereof are possiblewithout departing from the scope and spirit of the claims as hereafterrecited. In particular, different embodiments may include combinationsof features other than those described herein. Accordingly, the claimsare not limited to the foregoing discussion.

1-73. (canceled)
 74. A method for attempting at least one ofmotor-vehicle accident avoidance and motor-vehicle accident damageminimization, the method comprising: a. wirelessly transmitting, bynon-visual electromagnetic (EM) radiation and from a first motor-vehicledriven by a first driver, first factual input data; b. receiving thefirst factual input data at a second motor-vehicle driven by a seconddriver; c. in response to the receiving of the first factual input data,wirelessly transmitting by non-visual EM radiation and from the secondmotor-vehicle, second factual input data comprising blood-alcohol statedata of at least one of the first and second drivers; d. receiving thesecond factual input data by a third motor-vehicle; and e. in responseto the receiving of the second factual input data, performing by anonboard computer of the third motor-vehicle at least one vehicle controlaction so as to attempt at least one of the following: (i) avoidingbeing involved in a motor-vehicle accident, that is computationallypredicted to occur based on the blood-alcohol state data; and (ii)reducing damage inflicted upon the third motor-vehicle as a result ofinvolvement in the motor-vehicle accident that is computationallypredicted to occur based on the blood-alcohol state data.
 75. The methodof claim 74 wherein: (i) an onboard alcohol sensor is present in atleast one of the first and second motor vehicles; and (ii) theblood-alcohol state data is acquired by the onboard alcohol sensor. 76.The method of claim 74 wherein: (i) an onboard alcohol sensor configuredto detect driver blood-alcohol level by detecting an amount of alcoholin his/her perspiration is present in at least one of the first andsecond motor vehicles; and (ii) the blood-alcohol state data is acquiredby the onboard alcohol sensor.
 77. A method for responding to aprediction of a potential accident involving first, second and thirdmotor-vehicles, the method comprising: a. computationally predicting,based on blood-alcohol state data of a human driver and by an onboardcomputer of the first motor-vehicle, an accident scenario, the accidentscenario indicating that the first motor-vehicle might collide with thesecond motor-vehicle; b. in response to the predicting, wirelesslytransmitting, by non-visual electromagnetic (EM) radiation and from thefirst motor-vehicle, an accident alert; c. receiving the accident alertby the third motor-vehicle; and d. in response to the receiving of theaccident alert, attempting, by an onboard computer of the thirdmotor-vehicle, at least one of (i) avoiding colliding with the firstmotor-vehicle (ii) reducing damage inflicted upon the thirdmotor-vehicle resulting from colliding with the first motor-vehicle, byperforming at least one vehicle control action.
 78. The method of claim77 wherein: (i) an onboard alcohol sensor is present in at least one ofthe first and second motor vehicles; and (ii) the blood-alcohol statedata is acquired by the onboard alcohol sensor.
 79. The method of claim77 wherein: (i) an onboard alcohol sensor configured to detect driverblood-alcohol level by detecting an amount of alcohol in his/herperspiration is present in at least one of the first and second motorvehicles; and (ii) the blood-alcohol state data is acquired by theonboard alcohol sensor.
 80. The method of claim 77 wherein the first,second and third motor-vehicles are arranged so that (i) the secondmotor-vehicle is behind the first motor-vehicle and (ii) the firstmotor-vehicle is behind the third motor-vehicle.
 81. The method of claim80 wherein the accident scenario indicates that the first motor-vehiclemight be hit from behind by the second motor-vehicle.
 82. The method ofclaim 81 wherein the attempting of the onboard computer of the thirdmotor-vehicle is performed so as to avoid being hit from behind by thefirst motor-vehicle.
 83. The method of claim 81 wherein the attemptingof the onboard computer of the third motor-vehicle is performed so as toreduce damage inflicted upon the third motor-vehicle resulting frombeing hit from behind by the first motor-vehicle.
 84. A method forhandling a prediction that a first motor-vehicle accident involvingfirst and second motor-vehicles will occur, the method comprising: a.operating an onboard computer of the first motor-vehicle to predict,based on blood-alcohol state data of a human driver, that the firstmotor-vehicle accident between the first and second motor-vehicles willoccur; b. determining, by the onboard computer of the firstmotor-vehicle, if changing a velocity of the first motor-vehicle inorder to achieve at least one of the following: (i) avoid the firstmotor-vehicle accident, (ii) reduce a likelihood thereof, and (iii)reduce a severity thereof, would result in one or more of: A. a secondmotor-vehicle accident occurring between the first motor-vehicle and athird motor-vehicle; and B. an increase in a likelihood that the secondmotor-vehicle accident will occur; and c. in response to a positivedetermining, performing at least one vehicle control action by theonboard computer of the first motor-vehicle for adjusting the velocityof the first motor-vehicle according to at least one of: i. respectivevelocities of the second and third motor-vehicles; and ii. respectiveaccelerations of the second and third motor vehicles.
 85. The method ofclaim 84 wherein: (i) an onboard alcohol sensor is present in at leastone of the second and third motor vehicles; and (ii) the blood-alcoholstate data is acquired by the onboard alcohol sensor.
 86. The method ofclaim 84 wherein: (i) an onboard alcohol sensor configured to detectdriver blood-alcohol level by detecting an amount of alcohol in his/herperspiration is present in at least one of the second and third motorvehicles; and (ii) the blood-alcohol state data is acquired by theonboard alcohol sensor.
 87. A method for attempting at least one ofmotor-vehicle accident avoidance and motor-vehicle accident damageminimization, the method comprising: a. wirelessly transmitting, bynon-visual electromagnetic (EM) radiation and from a firstmotor-vehicle, factual input data comprising blood-alcohol state data ofa human driver of the first motor vehicle or of another motor vehicleother than the first motor-vehicle; b. receiving the factual input dataat a second motor-vehicle; and c. in response to the receiving of thefactual input data, performing by an onboard computer of the secondmotor-vehicle at least one vehicle control action so as to attempt atleast one of the following: (i) avoiding being involved in amotor-vehicle accident, that is computationally predicted to occur basedon the blood-alcohol state data; and (ii) reducing damage inflicted uponthe second motor-vehicle as a result of involvement in the motor-vehicleaccident that is computationally predicted to occur based on theblood-alcohol state data.
 88. The method of claim 87 wherein: (i) anonboard alcohol sensor is present in at least one of the first motorvehicle and the another motor vehicle; and (ii) the blood-alcohol statedata is acquired by the onboard alcohol sensor.
 89. The method of claim87 wherein: (i) an onboard alcohol sensor configured to detect driverblood-alcohol level by detecting an amount of alcohol in his/herperspiration is present in at least one of the first motor vehicle andthe another motor vehicle; and (ii) the blood-alcohol state data isacquired by the onboard alcohol sensor.